technical developments of in vivo proton magnetic ...€¦ · editing, two-dimensional...

Technical Developments of in vivo Proton Magnetic Resonance Spectroscopy

by

Karl Landheer

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Graduate Department of Medical Biophysics University of Toronto

Copyright © 2017 by Karl Landheer

iii

Abstract

Technical Developments of in vivo Proton Magnetic Resonance

Spectroscopy

Karl Landheer

Doctor of Philosophy

Graduate Department of Medical Biophysics

University of Toronto

2017

Proton magnetic resonance spectroscopy (MRS) is a powerful non-invasive technique to probe

the biochemistry of the brain and body. Because MRS signals are collected from metabolites in

concentrations on the order of a few mM in biological tissues, (rather than from water at

approximately 40 M concentration, as in magnetic resonance imaging) low signal-to-noise ratio

(SNR) poses a major challenge. In the face of this challenge, there is an ongoing need for

improved techniques to enhance the clinical applicability of MRS. One area of interest, for

example, involves using MRS data as a biomarker for the assessment of early response in

radiation therapy.

This thesis focuses on the development of three MRS techniques, with the ultimate goal

of this work being used in early radiation detection response protocols. First, a technique is

developed to extend single voxel MRS to MRS of a small number of voxels (eg. two) without the

need for spatial frequency encoding, using customized selective radiofrequency (RF) excitation

iv

and the localized sensitivity of multichannel receiver coils. The SNR efficiency of this technique

is demonstrated in phantoms, healthy adult volunteers and patients with brain cancer. Second,

a novel inversion and saturation recovery sequence is developed in conjunction with a spectral

editing module to estimate the longitudinal relaxation time (T1) of lactate in vivo at 3 T. Lactate

is of significant interest due to its role in cellular metabolism, and particularly its elevation in

tumors. The resulting T1 estimate enables further optimization of MRS protocols and assists in

estimating the absolute concentration of lactate from MRS data. Third, a diffusion-weighted

two-dimensional spectroscopy sequence is developed, subsequently referred to as DW-JPRESS.

This sequence, as well as an optimized processing pipeline, is demonstrated to provide

estimates of the apparent diffusion coefficient (ADC) of brain metabolites beyond those

typically accessible at 3 T, namely glutamate, myo-inositol and scyllo-inositol. The DW-JPRESS

data provide unique information concerning the local diffusion environment of each measured

metabolite, with the potential to characterize microstructural changes from different brain

pathologies including cancer. Collectively, the technical development undertaken in this thesis

promises to enhance future clinical applications of MRS, such as its use in distinguishing

between neoplastic and nonneoplastic lesions, or for assessing tumor recurrence.

v

Acknowledgments

First of all I would like to thank my supervisor, Dr. Simon Graham, for this thesis would not be

possible without his constant guidance, support, and commitment. I would also like to thank

my supervisory committee members, Dr. Charles Cunningham and Dr. Kullervo Hynynen, for

their encouragement and enthusiasm throughout my project, as well as Chuck’s happiness to

discuss anything related to RF pulses.

I would like to thank Jeff Stainsby and Dr. Albert Chen for their help navigating the tricky

world of EPIC. I thank Dr. Arjun Sahgal for being the most enthusiastic clinical collaborator I

could hope for, as well as his record-breaking email response times. Thanks to Fred Tam for his

technical assistance and Rafal Janik for always being willing to explain why what I’m doing is the

wrong way of doing things. Thanks to Justin Lau for his help in understanding the quantum side

of this research. Thanks to all the volunteers, in particular James Mester, Lech Skórski and Philip

Chen who spent several hundred hours lying still inside the magnet without any complaints.

Thanks to Dr. Diana Sima for her help with AQSES, and Dr. Martin Wilson for his unending and

insightful support on the TARQUIN help forums. Thanks to Rolf Schulte and Ben Geraghty for

their help in implementing ProFit.

Thanks to all my friends for their much needed comic relief and Sarah for her love and

support. Additionally I acknowledge the pivotal roles my brother, Alex, and my grandparents,

Ann, Percy David, and Cath have played in my life – without them I would not be where I am

today.

Finally I am deeply grateful for the support of my parents Karen and Dolf, who always

nurtured my curiosity and pushed me to do my best.

vi

Table of Contents

Contents

List of Tables ................................................................................................................................... ix

List of Figures ................................................................................................................................... x

List of Acronyms ............................................................................................................................. xii

Chapter 1 ......................................................................................................................................... 1

1.1 Introduction ........................................................................................................................ 1

1.2 Basic Physics of Nuclear Magnetic Resonance Spectroscopy ............................................. 1

1.2.1 Quantum Mechanics of a Spin ½ Particle in a Magnetic Field................................ 1

1.2.2 Product Operator Formalism .................................................................................. 7

1.2.3 Relaxation ............................................................................................................. 13

1.2.4 Spectral Editing ..................................................................................................... 15

1.2.5 Two-dimensional Spectroscopy ............................................................................ 16

1.2.6 Relationship of the Semi-Classical Formalism to the Quantum Mechanical Formalism ............................................................................................................. 17

1.3 In vivo MRS of the brain .................................................................................................... 20

1.3.1 In vivo MRS acquisition ......................................................................................... 20

1.3.2 Biochemistry of the Brain ..................................................................................... 23

1.3.3 Gliomas ................................................................................................................. 27

1.3.4 Other brain lesions ................................................................................................ 30

1.3.5 Spatial Localization of in vivo MRS ........................................................................ 31

1.4 Parallel Imaging ................................................................................................................. 35

1.5 Absolute Quantitative MRS .............................................................................................. 37

1.6 Diffusion-weighted MRS ................................................................................................... 38

vii

1.7 Hypotheses and Thesis Outline......................................................................................... 41

Chapter 2 ....................................................................................................................................... 43

2.1 Introduction ...................................................................................................................... 43

2.2 Materials and Methods ..................................................................................................... 44

2.3 Results ............................................................................................................................... 51

2.4 Discussion.......................................................................................................................... 59

2.5 Conclusions ....................................................................................................................... 61

Chapter 3 ....................................................................................................................................... 62

3.1 Introduction ...................................................................................................................... 62

3.2 Theory ............................................................................................................................... 64

3.3 Methods ............................................................................................................................ 65

3.4 Results ............................................................................................................................... 72

3.5 Discussion.......................................................................................................................... 78

3.6 Conclusions ....................................................................................................................... 83

Chapter 4 ....................................................................................................................................... 84

4.1 Introduction ...................................................................................................................... 84

4.2 Methods ............................................................................................................................ 86

4.3 Results ............................................................................................................................... 94

4.4 Discussion........................................................................................................................ 100

4.5 Conclusions ..................................................................................................................... 106

Chapter 5 ..................................................................................................................................... 107

5.1 Summary ......................................................................................................................... 107

5.2 Future Directions for CSSMRS ......................................................................................... 109

5.3 Future Directions for DW-JPRESS ................................................................................... 111

5.4 Early Radiation Treatment Response ............................................................................. 114

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5.5 Final Remarks .................................................................................................................. 115

Bibliography ................................................................................................................................ 116

ix

List of Tables

Table 2.1: Summary of brain tumor patients for Chapter 2 ......................................................... 50

Table 2.2: Quantified NAA values from PRESS and CSSMRS ........................................................ 57

Table 2.3: Quantified Cho values from PRESS and CSSMRS ......................................................... 57

Table 2.4: Quantified Cr values from PRESS and CSSMRS ............................................................ 58

Table 2.5: Quantified Lac values from PRESS and CSSMRS .......................................................... 58

Table 3.1: Summary of radiofrequency (RF) pulses in the prototype pulse sequence ................ 68

Table 3.2: The TI, TR and total scan time values for all experiments in Chapter 3. ..................... 71

Table 3.3: T1 values measured within the white matter in two healthy volunteers .................... 75

Table 3.4: Estimated T1 values from six patients with high grade glioma .................................... 77

Table 3.5: Estimated absolute concentration of metabolites from six high grade glioma patients

....................................................................................................................................................... 78

Table 4.1: ADC estimates obtained from the “BRAINO” phantom............................................... 96

Table 4.2: ADCs estimated from 6 subjects for 2D and 1D pipelines. .......................................... 98

x

List of Figures

Figure 1.1: Graphical representation of the effect of an RF pulse on spin states .......................... 6

Figure 1.2: The conversion of to with an RF pulse .................................................................. 8

Figure 1.3: Effects of chemical shift and J-coupling on the measured absorption spectrum for

lactate ........................................................................................................................................... 12

Figure 1.4: 2D JPRESS signal as a function of time (horizontal axis) obtained with maximum-echo

sampling ........................................................................................................................................ 18

Figure 1.5: Logarithmically-compressed contour plot of the JPRESS absorption spectrum from

the “Braino” MRS phantom .......................................................................................................... 19

Figure 1.6: Spectrum obtained from parietal brain tissue of a healthy volunteer using PRESS

with TE = 30 ms echo time at 3 T .................................................................................................. 26

Figure 1.7: Pulse sequence diagram for PRESS ............................................................................. 33

Figure 1.8: Spatial localization obtained from PRESS ................................................................... 34

Figure 1.9: The effect of the order of summing and phasing the individual excitations in DW-

MRS ............................................................................................................................................... 40

Figure 2.1: a) Pulse diagram for CSSMRS. b) An anatomical T1-weighted image of a patient with

nominal voxel locations overlaid .................................................................................................. 46

Figure 2.2: Spectra from both a healthy volunteer (a and b) and a brain cancer patient (c and d)

measured with both CSSMRS and PRESS ...................................................................................... 48

Figure 2.3: Spectra from a healthy volunteer at 30-milisecond echo time, obtained by using

both CSSMRS and PRESS ............................................................................................................... 53

Figure 2.4: The unapodized spectrum obtained from CSSMRS from a patient 1along with the fit

....................................................................................................................................................... 53

xi

Figure 2.5: Measured and simulated differences between the CSSMRS and PRESS measurement

for six different voxel separations ................................................................................................ 54

Figure 2.6: Simulated metabolite quantification values for seven different voxel separations .. 56

Figure 3.1: Spectroscopic pulse sequence for measuring T1 relaxation ....................................... 66

Figure 3.2: T1 – weighted anatomical image with voxel placement ............................................. 72

Figure 3.3: Inversion recovery results for “Braino” phantom for the four major metabolites

observed at TE = 144 ms (creatine, lactate, NAA, choline).......................................................... 74

Figure 3.4: Singlet and doublet spectra for two different inversion times from high grade glioma

patient ........................................................................................................................................... 76

Figure 4.1: DW-JPRESS pulse sequence for the a) initial echo time and b) intermediate kth echo

time ............................................................................................................................................... 88

Figure 4.2: Flow chart of the processing steps used to estimate ADCs from the raw DW-JPRESS

data ............................................................................................................................................... 90

Figure 4.3: a) Axial prescription and b) coronal prescription of the DW-JPRESS voxel ................ 95

Figure 4.4: Water-suppressed JPRESS spectra obtained from healthy volunteer for two different

b-values, along with fit and residual obtained from ProFit .......................................................... 97

Figure 4.5: Plot of ADCs estimated from the 2D pipeline versus those estimated from the 1D

pipeline ......................................................................................................................................... 99

Figure 5.1: Four voxel profile overlaid on an anatomical axial MR image ................................. 110

xii

List of Acronyms

Acronym Definition

ADC Apparent Diffusion Coefficient

Ala Alanine

Asc Ascorbic acid (Vitamin C)

Asp Aspartate

AQSES An automated quantitation of short echo time

MRS spectra

BASING Band Selective Inversion With Gradient

Dephasing

CHESS Chemical Shift Selective

Cho Choline

Cr Creatine

CSSMRS Constrained Source Space Magnetic

Resonance Spectroscopy

DW-JPRESS Diffusion-Weighted J-Resolved Spectroscopy

DW-MRS Diffusion-weighted Magnetic Resonance

Spectroscopy

FGRE Fast Gradient Echo

FOV Field of View

xiii

FSPGR Fast Spoiled Gradient Echo

GABA -amintobutyric acid

Gd-DTPA Gadopentetic acid

g-factor Geometry factor

GRAPPA Generalized Autocalibrating Partially Parallel

Acquisitions

Gln Glutamine

Glu Glutamate

Gly Glycine

Glx Glutamine + Glutamate

GPC Glycerophosphorylcholine

GSH Glutathione

HVSD Hankel Singular Value Decomposition

IR Inversion Recovery

JPRESS J-Resolved Spectroscopy

Lac Lactate

MFIR Modified Fast Inversion-Recovery

MRI Magnetic Resonance Imaging

MRS Magnetic Resonance Spectroscopy

xiv

NAA N-Acetylaspartic acid

NAAG N-acetylaspartylglutamate

NMR Nuclear Magnetic Resonance

PCh Phosphorylcholine

PE Phosphorylethanolamine

PRESS Point-Resolved Spectroscopy

ProFit Two-dimensional Prior-Knowledge Fitting

RF Radiofrequency pulse

Scy Scyllo-inositol

SENSE Sensitivity Encoding

SLR Shinnar-Le Roux

SNR Signal-to-Noise Ratio

STEAM Stimulated Echo Acquisition Mode

TARQUIN Totally Automatic Robust Quantitation in NMR

TE Echo Time

TI Inversion Time

tNAA N-acetylaspartate + N-acetylaspartylglutamate

TR Repetition Time

Tau Taurine

1

Chapter 1

1.1 Introduction

This thesis develops several technical advances in the field of in vivo magnetic resonance

spectroscopy (MRS), focusing on applications to the brain. The following introduction provides

an overview of the necessary physics required to motivate the research, and to understand the

research methodology and analysis described in subsequent chapters. The specific introductory

topics of interest are the basic physics of nuclear magnetic resonance spectroscopy, spectral

editing, two-dimensional spectroscopy, in vivo MRS, magnetic resonance parallel imaging,

absolute quantitative spectroscopy and the measurement of the diffusion of brain metabolites.

1.2 Basic Physics of Nuclear Magnetic Resonance Spectroscopy

The basic physics underlying MRS is the logical starting point to understand the work described

in this thesis. This foundation is provided by physics underlying nuclear magnetic resonance

(NMR) for spin ½ particles, which fully applies to all the chemical species of specific interest

within the thesis context. Although the common “semi-classical” formalism used in magnetic

resonance imaging (MRI) is sufficient to describe the spin dynamics of uncoupled spin systems,

many of the chemical species of interest exhibit coupling phenomena. Thus, the full quantum

formalism must be summarized. A section of the introduction is also devoted to translating

between the semi-classical and quantum formalism, as the semi-classical formalism is used for

simplicity within this work when the effects of coupling can be neglected.

1.2.1 Quantum Mechanics of a Spin ½ Particle in a Magnetic Field

Elementary particles contain an intrinsic angular momentum referred to as spin. It is taken here

as an empirical fact that protons have an intrinsic angular momentum of ½ℏ, where ℏ is

Planck’s reduced constant (

6.626 x 10-34 m2 kg/s). The factor ℏ is frequently omitted due to

convenience and is implicitly assumed, thus a proton is hereafter referred to as a spin ½

particle, or in MR jargon as a “spin”.

2

Every NMR experiment measures how a group of spins evolve with time. A spin ½

particle in the presence of an applied magnetic field (by convention chosen to be along the z

direction, also referred to as the longitudinal direction) can occupy one of two stationary

Zeeman eigenstates of energy: one aligned with the field, referred to as the “spin up” state, and

one anti-aligned with the magnetic field, referred to as “spin down” state. These two

eigenstates are conveniently expressed using either the “bra-ket” notation or the vector

notation:

(

)

(1.1)

(

)

(1.2)

where is aligned with the field and is anti-aligned. These two spin states are referred

to as the Zeeman eigenbasis because any spin state can be expressed as a linear combination of

these two. The second value in the bra-ket notation indicates the projection of the spin angular

momentum onto the z-axis, as chosen by convention.

The matrix representations of the three angular momentum operators in the Zeeman

eigenbasis are

ℏ

(

)

(1.3)

ℏ

(

)

(1.4)

ℏ

(

)

(1.5)

It can easily be shown that the application of the z angular momentum operator to the spin

state or yields the eigenvalues ℏ/2 and -ℏ/2, respectively. By the Heisenberg

uncertainty principle, a spin cannot be in simultaneous eigenstates of all three operators, since

the operators do not commute. This is evident as the states or are not eigenvectors

3

of or , which provide the components of angular momentum in the “transverse plane”

orthogonal to the longitudinal direction.

The Hamiltonian for a single spin in the presence of an applied magnetic field is given by1

ℏ (

)

(1.6)

where is the gyromagnetic ratio (267.5 x 106 rad/s/T for protons) and is the magnitude of

the applied magnetic field. Application of this Hamiltonian to the two eigenstates yields the two

energy eigenvalues:

ℏ

(1.7)

ℏ

(1.8)

Thus, the spin-down state is at the higher energy state and the spin-up state is at the lower

energy state. The difference between the two energy levels is ℏ ℏ and spins can

undergo transitions between the two states when this amount of energy is applied in the form

of a radiofrequency (RF) pulse, as explained further below.

Although there are only two eigenstates for a spin ½ particle, a very small number of

spins in a typical NMR experiment are actually in either of the eigenstates due to various time-

dependent processes, such as molecular motion. The vast majority of spins are in a

superposition of spin-up and spin-down states, which can be written using the superposition

principle as

(1.9)

where and are complex numbers called position coefficients. These coefficients must be

normalized such that:

| |

(1.10)

4

The spin state obeys the time-dependent Schrödinger equation1:

( ) ( )

(1.11)

where is the spin Hamiltonian operator, the operator representation of the total energy of

the system. Schrödinger’s time-dependent equation is a first-order ordinary differential

equation with the solution,

( ) ( ) ( ) (1.12)

Equation 1.12 indicates that the spin state at a later time ( ) is completely determined by

knowledge of the initial spin state at time and the Hamiltonian of the system.

Typically the dynamics of spins are expressed in the “rotating reference frame” which

simplifies the solution of the Schrödinger equation in the presence of an applied RF pulse. This

is done by viewing the experiment from a frame that rotates about the -z axis at a chosen

reference angular frequency, . It can then be shown1 that the solution to the Schrödinger

equation in the rotating frame is given by

( ) ( ) ( ) (1.13)

where is the Hamiltonian in the rotating frame and is the state of the spin in the

rotating frame, given by

| ( )| (1.14)

and is the rotation matrix about the z axis in the rotating frame. The Hamiltonian in the

rotating frame, , can be related to the Hamiltonian in the laboratory frame by1

( ) ( ) (1.15)

The convenience of the rotating frame is not immediately clear from Equations 1.13 – 1.15.

However, if a circularly-polarized RF pulse is applied at the chosen reference angular frequency

, using an electromagnetic coil of appropriate geometry such that magnet field

5

components of the pulse rotate in the transverse plane, then the Hamiltonian in the laboratory

frame is given by1

{ ( ) ( )}

(1.16)

where is the amplitude of the applied RF pulse (given in units of Tesla), and is a chosen

phase factor that controls the orientation of the effect of the RF pulse. It can be shown by

applying Equation 1.16 to Equation 1.15 that the effect of the Hamiltonian of the RF pulse can

be written in the rotating frame as

( )

( ){ ( ) ( )}

(1.17)

where is the amplitude of the magnetic field of the applied RF pulse. It can then be shown by

applying Equation 1.17 to Equation 1.13 that the state after application of the RF pulse is

related to the state before application of the RF pulse by

( ) ( ) ( ) (1.18)

where ( ) is the rotation operator with matrix representation

( ) ( ( ) ( ) ( )

( ) ( ) ( ))

(1.19)

Equation 1.19 shows that the effect of an RF pulse is the rotation of the state of the particle in

the rotating frame by the “flip angle” . For all work presented here the phase factor is

arbitrarily set to be zero, thus the effects of the RF pulses are to rotate the spin state around

the x axis. If the phase factor was changed to then the effect of RF pulses would be to

rotate the spin state about the y axis instead. Modern NMR instrumentation provides full

control over the phase of the applied RF pulses, but for all the pulse sequences presented here

there is no such need, thus it is neglected for simplicity. For an amplitude-modulated pulse

( ) is given by

6

∫ ( )

(1.20)

where is the RF pulse duration. A graphical representation of the effects of Equation 1.19 is

shown in Figure 1.1, depicting how common RF pulses act on spins initially in state . A

rotation about the x axis by radians places spins in the state , which is an eigenstate

of the angular momentum operator . A further /2 rotation places spins in the state .

Figure 1.1: Graphical representation of the effect of Equation 1.19 (an RF pulse causes rotation

of spin states, represented by dark arrows, about the x axis in the rotating frame when the

phase equals zero). The initial, spin-up state is ; is an eigenstate of the

momentum operator ; is the spin-down state. x is the rotation operator about the x

axis with brackets indicating the flip angle by which the angular momentum operator is

rotated, according to Equation 1.20.

7

Alternatively, spins in the state can be placed into state by a rotation about the x

axis.

1.2.2 Product Operator Formalism

In a typical in vivo MRS experiment the chemical species are present in concentrations of a few

millimoles, which means the signal from roughly 1020 spins is measured. Two “tricks” have been

developed to circumvent the need to develop an eigenstate that spans all 1020 particles. These

two tricks are referred to as the density matrix and product operator formalism2. The density

matrix formalism is complete and is able to describe the result of any general NMR experiment.

The product operator formalism is only applicable in the “weak-coupling” regime, where the

Hamiltonian is dominated by the Zeeman term. In this scenario, all terms within the

Hamiltonian commute. This approximation is sufficient for this thesis as most biological

molecules are weakly-coupled. The benefit of the product operator formalism is that an implicit

expectation value is taken over many spins. This gives a clear physical meaning to the angular

momentum operators and much of the subsequent quantum mechanics can be ignored,

including the density matrix.

In the following, the terms and refer to the longitudinal, x and y

magnetization components of spin k, respectively. Strictly, spin k is not a single spin but the

average over all magnetically equivalent spins. For example, spin k could represent all the

hydrogens in water or the methyl protons in lactate. The measured signal from spin k in an MRS

experiment is given by (i.e., only magnetization in the transverse plane is measured).

The relevant Hamiltonians can be summarized by a few simple rules explained below. This

formalism offers the ability to predict the outcome of complicated multi-pulse experiments for

weakly-coupled spins with the successive application of simple rules. The effect of RF pulses on

the magnetization terms is identical for the individual spin states, as all individual spin states

experience the same rotation, as shown in Figure 1.2.

8

Figure 1.2: The conversion of to produced by an RF pulse. The net magnetization in these

directions is represented by the grey arrows. Notice that the effect of the RF pulses, ( )

and ( ) is identical for each different spin.

The product operator analog of Equation 1.13 is given by

( ) ( ) ( ) ( ) (1.21)

After application of an RF pulse, the spins are said to be in “free precession”. For a typical in

vivo proton MRS experiment involving N magnetically different types of spins in an isotropic

liquid, this condition is governed by two relevant terms in the Hamiltonian:

∑

∑

(1.22)

where is the spin angular momentum vector for the kth spin; is the scalar J-coupling value

between two different spins, a measured (field-independent) constant; and is the relative

Larmor frequency given by

(1.23)

where , which is dependent on the magnetic field experienced by the particular spin

and is a reference frequency which is under experimental control, typically tuned to water

9

due to its high signal. The first term in Equation 1.22 is referred to as the “chemical shift term”,

resulting from shielding of the applied magnetic field by the electron cloud surrounding the

proton nucleus. This value is by convention measured in reference to the molecule

tetramethylsilane (TMS) and is usually stated in parts per million (ppm). The conversion

between the angular frequency, measured in rad/s, and the chemical shift measured in ppm

is given by

(1.24)

where is the precessional frequency of the protons in TMS. Measuring chemical shift in

ppm is convenient because the magnetic field dependence of the frequency of spins is

removed.

The second term in Equation 1.22 characterizes the “J-coupling” interaction that arises

between neighboring nuclear spins from an indirect coupling that is mediated by the

surrounding electron cloud. Briefly, each spin has its own associated magnetic field which

slightly alters the electromagnetic characteristics of the surrounding electron cloud. This

altered electron cloud then affects the local magnetic field of the coupled spin, slightly changing

its precessional frequency. This mediation through the electron cloud is why J-coupling is also

referred to as indirect coupling. (As an aside, each spin can affect the local magnetic field of a

neighboring spin and is referred to as “direct dipole-dipole coupling”, or simply “dipole-dipole

coupling”. In an isotropic liquid it can be shown that the dipole-dipole effect averages to zero

and therefore does not affect the observed spectrum beyond influencing the rate of

“relaxation” processes1. More will be said about relaxation processes below.)

Using the product operator formalism by applying the Hamiltonian given in Equation

1.22 to Equation 1.21 the freely precessing magnetization of one spin weakly-coupled to

another (in the absence of relaxation), referred to as an “AX system”, is given by1

10

→( ( )( ( ) ( ) ))

( ( )( ( ) ( ) ))

(1.25)

→( ( )( ( ) ( ) ))

( ( )( ( ) ( ) ))

(1.26)

→

(1.27)

with similar transformations for and . Equations 1.25 and 1.26 are generated from

several other assumptions beyond weak spin coupling. The RF pulse duration is considered to

be short in comparison to both longitudinal and transverse relaxation processes. This is a good

approximation for the RF pulses used in this thesis, which have duration of 3-30 ms. For

substantially longer RF pulses, the effects of relaxation must be included for quantitative

measurements. Furthermore, for simplicity when applying the rules of the product operator

formalism, relaxation is often suppressed at the outset then added post-hoc by following the

appropriate echo pathway3 which generated the acquired signal. As can be seen from the time-

evolution of the transverse operators and (Equations 1.25 and 1.26), the terms ( )

and ( ) are to the chemical shift evolution, and terms ( ) and ( ) are due

to J-coupling evolution. The chemical shift evolution is simply an accrual of phase

(interconversion of and ), whereas J-coupling results in an inter-conversion from in-phase

magnetization ( ) to anti-phase magnetization ( , ). The term does not

evolve under chemical shift or J-coupling. It should also be mentioned that the process of spin

echo formation discussed above must be qualified slightly when J-coupled species are

considered. By allowing magnetization to evolve in the transverse plane for time and

then applying a pulse, the resulting magnetization after another evolution time is still

modulated by J-coupling effects whereas chemical shift effects are completely refocused. This is

what allows for J-resolved spectroscopy, as discussed in further detail in Section 1.2.4.

11

The most common in vivo MRS experiments use either point resolved spectroscopy

(PRESS)4 or stimulated echo acquisition mode (STEAM)5 sequences. Both sequences use

identical spatial localization techniques to obtain MRS data from a single coarse volume

element (voxel), but PRESS involves spin echo formation (a pulse followed by two pulses)

whereas STEAM uses a stimulated echo involving three pulses. The PRESS sequence offers

a factor of two improvement in the amplitude of the signal, but STEAM uses shorter RF pulses

(since, for a given max B1 amplitude, a pulse will be of shorter duration as evident from

Equation 1.20) and thus the echo time can be reduced, making STEAM better suited for

chemical species whose signal decays rapidly. Additionally pulses deposit a quarter of the

energy when compared to pulses and have more flexibility in the choice of bandwidth and

slice profile. Due to their inherent similarities STEAM, with suitable modifications, could be

used interchangeably for all the work presented here.

After the MRS signal is acquired in the time domain by PRESS or STEAM sequences, the

spectral information is usually interpreted in the frequency domain after Fourier

transformation of the signal. In the frequency domain, different chemical species are present at

their characteristic chemical shifts in the form of Lorentzian functions. The Lorentzian nature is

due to the monoexponential decay of the transverse magnetization, and deviation from this

monoexponential decay will result in other lineshapes. By taking the Fourier transform of a

measured time-dependent MRS signal with a relative frequency and transverse relaxation

value T2, the following spectral lineshape is obtained:

{ ( ) (

)}

( )

( )

( )

( )

( )

(1.28)

The real portion of Equation 1.28 is a Lorentzian lineshape, and is referred to as the

“absorption” portion of the spectrum, whereas the imaginary portion is referred to as the

“dispersion” portion. Of the two portions, the absorption lineshape has much narrower

linewidth and is conventionally displayed as a consequence. All spectra in this thesis consist of

absorption lineshapes. The measured signal is multiplied by an arbitrary phase factor to display

12

Figure 1.3: Effects of chemical shift and J-coupling on the measured absorption spectrum for

lactate. A molecule of lactate has two chemical groups which give measurable proton MRS

signal: a methine [CH] and a methyl [CH3] group. The methine proton gives rise to a quartet (left

side of the bottom spectrum) because it is coupled to three protons, whereas the methyl group

is a doublet (right side of the bottom spectrum) because it is coupled to a single proton. The

area under the doublet is three times as large as that under the quartet, due to the factor of

three times as many protons from the methyl group than the methane group. The chemical

shift and J-coupling value are not shown to scale, for display purposes. The hydroxyl group does

not contribute to measurable signal at room temperature due to its very fast T2 relaxation.

13

the real portion as the Lorentzian lineshape, which is referred to as “zero-order phasing” the

spectrum.

Considering now the MRS data from multiple weakly-coupled species, the resulting

absorption spectrum will consist of a set of Lorentzians separated by the relative chemical shift,

, of each species, with the Lorentzians split further by the J-coupling effects that occur

between each species. A peak which has no J-coupling is referred to as a “singlet”, and a peak

which is split into two peaks (due to its coupling with a single neighboring spin) is referred to as

a “doublet”. A peak which is split into three peaks (due to coupling with two neighboring spins

from the same nuclei) is referred to as a triplet, etc. The relative amplitudes of the split lines are

given by the binomial distribution. Splitting is additive: for example, if a spin is coupled to two

different nuclei each with a single spin, the resulting splitting would produce a doublet of

doublets (a quartet) with four spectral lines of identical amplitude. As another example, Figure

1.3 shows the effects of chemical shift and J-coupling on the spectrum from lactate.

1.2.3 Relaxation

Both spin-lattice (T1) and spin-spin (T2) relaxation are caused by fluctuations in the magnetic

field experienced by the spins due to thermal molecular motion. The T1 relaxation effect

characterizes the restoration (or “recovery”) of magnetization toward the equilibrium state of

alignment with the applied main magnetic field. This “longitudinal relaxation” occurs because

the spin state aligned with the field minimizes potential energy and after each thermal collision,

a small amount of energy is lost such that the magnetization will very slightly preferentially

align with the field. After very many collisions, the original thermal equilibrium magnetization is

restored. In practice, T1 is an empirically measured parameter on the order of 1000 ms for most

of the biologically relevant molecules considered in this thesis. The other relaxation parameter,

T2 characterizes how each molecular collision affects the phase of the individual spins. Initially

after RF excitation, a net polarization exists indicating that the spins are freely precessing “in

phase” in the transverse plane. After a molecular collision between spins, however, the phase

of the spins is altered. After many collisions, the phase across all spins approaches a uniform

distribution and the transverse magnetization decays (relaxes) to zero. For the biological

chemicals described here, T2 is typically on the order of 100 ms, although the value varies

14

significantly across chemical species. The T1 and T2 values for a group of spins are affected by

numerous physical factors such as the temperature, magnetic field strength, and the

surrounding molecular environment. Some chemical agents, such as the commonly used

contrast agent gadolinium, can drastically shorten the T1 of surrounding molecules due to its

unpaired electrons which cause magnetic field variations that induce relaxation.

The two most common NMR methods to measure longitudinal relaxation are the

Inversion Recovery6 and Saturation Recovery7 sequences of RF pulses. The Inversion Recovery

sequence initiates with an RF pulse of flip angle, referred to as an “inversion pulse” for its

ability to make the equilibrium magnetization point in the opposite direction. The time

between the inversion pulse and a subsequent RF excitation pulse, used to measure or

“read out” the NMR signal, is referred to as the inversion time (TI). The signal is measured at a

variety of TI values and then fit to a mono-exponential model to estimate the T1 value. In a

typical Inversion Recovery experiment, the magnetization is allowed to relax very nearly to

equilibrium between successive measurements at different TI values. Thus, the repetition time

(TR) is usually set to a value much greater than the T1 value of interest. In a Saturation Recovery

experiment, the signal is measured at a variety of different TR values, where the signal is not

allowed to relax to equilibrium in between successive RF excitations, and fit to a mono-

exponential model to estimate T2.

The T2 relaxation is typically measured via a spin-echo pulse sequence7. In this

experiment, the magnetization is excited by a RF excitation pulse and then allowed to relax

(de-phase) in the transverse plane for a duration . A pulse, referred to as a “refocusing

pulse” is then applied to flip the orientation of spins in the transverse plane. As the spins retain

the same precession characteristics after refocusing as they had before (see below for further

clarification), the magnetization re-phases at a time afterwards when a “spin-echo” is

said to have formed. To estimate T2 values, this spin echo sequence can be repeated for many

different values and the measured spin signal amplitudes can then be fit to a

monoexponential model as a function of TE. Alternatively, it is also possible to sample the T2

decay curve by creating multiple spin echoes with a single RF excitation pulse followed by

an appropriately-spaced train of refocusing pulses. These methods are in no way exhaustive,

15

as virtually any pulse sequence which causes magnetization to evolve in the transverse plane

and longitudinal plane will be sensitive to both T1 and T2, and in principle can be used to

perform relaxation measurements. However, the pulse sequences described have the merit

that they enable measurements of a single type of relaxation in a relatively straightforward

manner.

1.2.4 Spectral Editing

One challenge of in vivo MRS is that many of the chemical species of interest have very similar

chemical shift values. Thus, the overlap of spectral lines causes difficulties in accurately

separating the signals and quantifying relative concentrations. One method to overcome this

pitfall is referred to as “spectral editing”, which has been applied to a wide array of different

chemical species. Spectral editing falls into two categories: J-difference editing and multiple

quantum coherence editing. The former is used within this thesis and is subsequently discussed

below, whereas the latter is rarely used for in vivo MRS due to time constraints as well as

hardware limitations. The J-difference editing category exploits the differences in the J-coupling

between metabolites with comparable chemical shift. Initially, this spectral editing technique

used different echo times to produce different evolutions through J-coupling8. For example, it

can be shown that for an AX system (two individual weakly coupled spins) a spin-echo

experiment can be represented by

→ ( )

(1.29)

If the pulse sequence is repeated with twice the evolution time, then

→ ( )

(1.30)

The subtraction of the measured signal from these two experiments yields the magnetization

( ). For spins that are uncoupled, the resulting measured magnetization is (

) for both evolution times, thus subtraction eliminates the signal component from

uncoupled spins. This neglects transverse relaxation effects, however, as the measurement

16

with the longer echo time will have reduced signal. To circumvent this problem, improved

spectral editing techniques have been developed which use frequency-selective RF pulses to

refocus J-coupling effects9. These spectral editing techniques selectively edit one spin thereby

modifying the measured magnetization of the associated coupled spin. Typically two cycles are

repeated, one with the editing pulses off, and one with the editing pulses on. The

measurements are then subtracted to yield the J-coupled spin of interest. The results from

these selective-editing sequences are similar to the original spectral editing method, but are

insensitive to T2 relaxation effects because both cycles have equal echo time. This spectral

editing technique is used in Chapter 3 for the separation of the lactate methyl group and

contaminating lipid signals.

1.2.5 Two-dimensional Spectroscopy

An alternative to spectral editing pulses is two-dimensional (2D) MRS. Similar to spectral editing

techniques, 2D spectroscopy techniques exploit J-coupling to distinguish between overlapping

metabolites10. Used widely in the field of in vitro NMR, 2D MRS offers significantly more

information over 1D spectroscopy, such as molecular connectivities through correlation

spectroscopy (COSY11) and molecular distances through Nuclear Overhauser Effect

spectroscopy (NOESY12). Due to hardware constraints and scanning time, however, only the

most basic of 2D spectroscopy techniques have been applied in vivo13. The 2D spectroscopic

technique referred to as J-resolved spectroscopy (JPRESS) is used within this work due to its

comparatively shorter 2D spectroscopic acquisition time compared to other alternatives13

together with 2D spectral fitting software referred to as ProFit14,15.

The JPRESS experiment can be understood by considering how the magnetization of J-

coupled species evolves with time. From Equation 1.25 and Equation 1.26, J-coupling results in

an interconversion of in-phase and anti-phase magnetization. The resultant magnetization for a

spin-echo experiment with echo time with a reference frequency tuned to the Larmor

frequency of the spin is given by

→ ( ) ( )

(1.31)

17

Thus, the measured signal is modulated by the cosine term ( ) due to J-coupling.

The second term in Equation 1.31, the anti-phase magnetization, is not directly measured but

can be detected by converting it to in-phase magnetization through RF pulses or time-

evolution. By measuring the signal with sequentially increasing echo time, , metabolites that

overlap in chemical shift can be discerned by their J-coupling value. In principle, in vivo JPRESS

uses two pulses for spatial localization as necessitated by PRESS4 but the result of Equation

1.31 still holds. Typically, in vivo JPRESS spectra are acquired with a

maximum echo sampling scheme (data collection begins immediately after the last crusher

gradient, instead of the usual 1D approach of acquiring beginning at the peak of the echo) to

increase available SNR as well as to improve sensitivity by shifting the tails of the absorption

lineshapes off the horizontal axis16. Figure 1.5 displays an example 2D JPRESS signal obtained

over time from a “phantom” test object. The corresponding spectral data after Fourier

Transformation are shown in Figure 1.5. The directly measured frequency dimension is referred

to as (chemical shift) and the indirectly measured frequency dimension (from the sequential

increase in echo time) is referred to as (J-modulation). The benefits of acquiring with a

maximum echo sampling scheme can be readily observed from Figure 1.4 and Figure 1.5, as the

ripples in the time-domain data ensure that the tails of the lineshapes are now oriented at 45o.

If data sampling commenced at the spin echo maximum, then the tails would lie on the F1 = 0

axis, contaminating the measurement of smaller resonances along this line. The tails are

inherently lengthy due to the Lorentzian nature of the absorption spectrum, caused by taking

the Fourier transform of the exponential envelope of the time-dependent signal recorded

during JPRESS.

1.2.6 Relationship of the Semi-Classical Formalism to the Quantum Mechanical Formalism

In the classical formalism, the magnetization is governed by the classical equivalent of the

Schrödinger equation, the Bloch equations:

( ) ( ( ) ( ))

( )

(1.32)

18

( ) ( ( ) ( ))

( )

(1.33)

( ) ( ( ) ( ))

( )

(1.34)

where is the equilibrium magnetization value, and the magnetic field ( )

( ) ( ) . The terms ( ) and ( ) represent the magnetic field of

the RF pulse, and the terms , and are the longitudinal magnetic field gradients in the

Figure 1.4: 2D JPRESS signal as a function of time (horizontal axis) obtained with maximum-

echo sampling. The data were acquired with a total of 100 different echo times (“echo steps”,

vertical axis), each with an acquisition time of 409.6 ms. The Fourier transform of these data is

shown in Figure 1.5.

19

Figure 1.5: Logarithmically-compressed contour plot of the JPRESS absorption spectrum from

the “Braino” MRS phantom. The x axis is the chemical shift frequency dimension (measured in

ppm), and the y axis is the J-modulation frequency (measured in Hz because it is field-

independent).

three orthogonal directions. These gradient terms are critical for in vivo MRS, as they are used

to together with RF pulses to select regions of interest and to de-phase the signal from other

regions4,5, as explained in more detail in Section 1.3.5.

The classical formalism involves simpler equations than the quantum mechanical

formalism and thus is preferential when small effects such as J-coupling can be neglected. For

proton magnetic resonance imaging (MRI) which measures signal almost entirely from the

uncoupled protons of water and fat, or when the applied RF pulse dominates the Hamiltonian

(and thus J-coupling effects can be neglected) the classical formalism is the obvious method of

choice. Solving the Bloch equations reveals that the magnetization behaves identically to the

quantum mechanical formalism in the absence of coupling, namely RF pulses rotate the

magnetization, transverse magnetization (the measured portion of the magnetization) accrues

phase with time and relaxation of the magnetization vector components is identical to

relaxation of the angular momentum operator analogs. It is evident from solving Equations

20

1.32 – 1.34 that the transverse magnetization decays exponentially whereas the longitudinal

magnetization recovers exponentially.

In the semi-classical formalism the detected signal from a coil, ( ), is given by the

equation

( ) ( ( ) ( ( ) ( ) )

(1.35)

where ( ) is the vector field produced by a unit current of one of the fields at the position, .

This is a statement of Faraday’s law combined with the principal of reciprocity. In particular it is

the free-precession behavior (accrual of phase with time) of the magnetization that induces the

signal. For the quantum mechanical picture a similar expression can be developed using the

angular momentum operators instead. In a modern clinical scanner it is typical to receive the

signal using an array of 8 to 64 different channels. Data from these channels are then combined

to give a single measured signal from the sample.

1.3 In vivo MRS of the brain

1.3.1 In vivo MRS acquisition

The previous section described the physics required for in vivo MRS. In this section, applications

of MRS will be investigated focusing on the brain and brain tumors, due to the relevance with

the rest of the thesis. Because of its biological abundance and strong NMR signal, the proton,

also referred to as 1-Hydrogen (1H), is the nucleus of choice for MRS although 31P, and 13C are

also used in research settings17. To reiterate, the chemical environment slightly alters the

magnetic field experienced by the nucleus18 in the form of chemical shift and J-coupling effects,

as shown by the Hamiltonian in Equation 1.22. The measured signal in space and time is then a

superposition of precessing magnetization at several different frequencies for each chemical

species, also referred to as “compounds” or “metabolites”. Spectroscopy can be used to assess

non-invasively the in vivo relative concentrations of these different compounds, providing

additional biological information beyond anatomical MRI. Because these metabolites are found

at concentrations much smaller than water (on the order of a few mM) it is important to

21

remove the water signal so that dynamic range issues do not occur. The most common method

of water removal for in vivo MRS involves a chemical shift selective (CHESS) module19, which

selectively excites water and then dephases it with a large gradient.

The typical processing steps in almost every in vivo MRS experiment are:

Phasing of the data acquired from each coil element prior to summation by the inverse

of the coil sensitivity phase. The coil sensitivity is typically estimated from a water-

unsuppressed measurement, and the lack of this step can drastically reduce SNR if there

is a significant variation in phase across coils.

Water removal via Hankel singular value decomposition. Despite the use of a water

suppression module, there typically remains some residual water signal which can be

several times larger than any of the metabolites. The long tail of the residual water

lineshape can contaminate MRS results, and thus requires this additional processing

step.

Remove eddy currents (as explained below), either using a water-unsuppressed

acquisition20 or analytical methods21.

Zero filling, typically by a factor of two (appending zeros to the end of the measured

signal). This increases the spectral resolution by a factor of two after the measured

signals are Fourier transformed.

Fourier transformation of the signal to obtain the spectrum.

Zero order phasing (applying a single phase factor to the entire spectrum) to separate

the absorption (real) and dispersion (imaginary) line shapes.

First order phasing (applying a linear phase variation across the spectrum) to correct for

how the digital sampling comb used to measure the signal does not coincide precisely

with the spin echo maximum.

22

Reduction of spectral noise by mathematical convolution (optional). Convolution is

typically performed with a Gaussian kernel of 1 or 2 Hz full-width at half-maximum at

the cost of broader linewidths.

There are a variety of commonly encountered artefacts which can cause erroneous

quantification in both 1D and 2D in vivo MRS. Below is a summary of some of the most relevant

artefacts typically encountered, however a more comprehensive examination of all

encountered artefacts is available in the literature22.

Motion artefacts arise due to the movement of the subject either in-between or during

scans. Some degree of motion is unavoidable in in vivo experiments but attempts should

be taken to minimize excessive motion as it results in increased linewidths, improper

voxel localization and decreased water suppression efficacy as well as a reduction in the

peak area due to phase cancellation23. Some degree of motion can be corrected by

aligning peaks prior to averaging the spectra, however motion which happens during a

single acquisition cannot be fully corrected in this manner, as it will lead to signal

cancellation due to gradient dephasing, as explained further in Section 1.6.

Unbalanced crusher gradients can be caused by an improperly tuned final crusher

gradient or due to eddy currents. Unbalanced gradients manifest as a shift of the peak

of the echo, resulting in an alteration of the Lorentzian lineshape of the spectrum. This

can be corrected for post-acquisition by first order phasing, however the underlying

cause may also result in other artefacts.

Chemical shift artefact is caused by the frequency-selective RF localization pulses

exciting slightly different locations for different metabolite peaks. For singlets, this

results only in a positional shift of the voxel, however for coupled metabolites such as

lactate, it results in a positional shift as well as changes in phase and amplitude at the

edges of the voxel (referred to as anomalous J-modulation24). This can be corrected by

using larger bandwidth pulses or by inner volume saturation25, where voxel boundaries

are defined by the edges of saturation pulses instead of through the intersection of

three frequency-selective RF pulses.

23

Spurious echoes are the result of the refocusing of water or fat signals from outside the

voxel. When shimming to minimize the magnetic field homogeneity across a small voxel

a large set of linear shim gradients may be applied, which can shift the water resonance

of regions outside the voxel so that they are no longer captured within the water

suppression band. Because the water concentration is approximately 10,000 times

greater than the metabolites, even if a small fraction of the water signal is unspoiled it

can be comparable to the metabolite amplitudes. Spurious echoes can be effectively

eliminated by customizing the spoiling power and directions for the particular scan26.

Eddy currents are small electrical currents in the sample which are induced by changing

magnetic fields (such as the switching of gradients), inducing their own local magnetic

field fluctuations. As mentioned above, they can cause unbalanced gradient crushers

and they can also induce large phase distortions over the acquisition of the signal. Phase

distortions induced by eddy currents can be corrected, most commonly by using a

water-unsuppressed signal to estimate the phase distortion and deconvolving this

component from the spectra.

1.3.2 Biochemistry of the Brain

The exact roles of all the metabolites measurable by MRS are unknown. However, MRS has

played a critical role in improving understanding of the physiology of many brain metabolites.

This has been achieved due to the high specificity, non-invasiveness and minimal attributable

risk of MRS, permitting measurement of healthy volunteers as well as patients. A summary of

the most commonly measured metabolites is given below, although this is non-exhaustive as

virtually any molecular compound with hydrogen groups with sufficiently long T2 present in

concentrations of ~1mM or greater can in principle be measured by MRS. A more

comprehensive list of 35 metabolites that can be detected with 1H MRS, as well as the chemical

shifts, J-coupling patterns of each metabolite and full in vitro spectra can be obtained from

Govindaraju et al.27

The five brain metabolites most commonly measured by MRS are: N-Acetylaspartic acid

(NAA), myo-inositol (mI), choline (Cho), creatine (Cr) and lactate (Lac). Of these metabolites,

24

NAA exhibits the highest spectral amplitude in a healthy adult brain. This compound is

produced in the mitochondria of neurons and transported into the neural cytoplasm. The NAA

molecule is the second most abundant amino acid in the brain27 (the first being glutamic acid)

and is characterized in MRS by strong resonance at 2.0 ppm. Although its exact function is not

fully understood, NAA is believed to act as an osmolyte as well as a precursor to N-

acetylaspartylglutamate (NAAG), which is believed to be involved in glutamatergic

neurotransmission28. The NAA signal is treated as a biomarker of neuronal density, with a

reduction from normal levels reflecting either neuronal loss or dysfunction27.

Myo-inositol has several prominent resonances at ~3.5 ppm, and is primarily found

within glial cells,29 which are support cells within the brain. Myo-inositol is the most abundant

isomer of inositol and is an osmolyte and precursor to membrane phospho-inositides,

phospholipids and myelin sheet structures30, and plays a key role in signal transduction31. The

exact roles of mI are unknown, although it appears to be a storage form of glucose32. Myo-

inositol has been found to be increased in amyotrophic lateral sclerosis (ALS), with the ratio of

NAA/mI being decreased by 22% (P = 0.001) compared to healthy tissue33.

Creatine has two prominent singlets at 3.0 and 3.9 ppm, and is associated with energy

metabolism through its role in phosphocreatine phosphorylating adenodiphosphate into

adenotriphosphate in times of high energy demand27. Creatine is often used as a control for the

changes in other metabolites measured in MRS, as it does not vary significantly with age34 and a

variety of diseases. However, some diseases can affect Cr levels, such as brain tumors35, which

can confound ratios36.

Choline (Cho) has a strong peak at 3.2 ppm and is the precursor of the active form

phosphorylcholine37 (PCh), which plays a role in cell membrane turnover, and in the production

of acetyl-choline38, an excitatory neurotransmitter that plays a role in memory and learning39.

Typically, glycerophosphorylcholine (GPC) and PCh cannot be distinguished by in vivo MRS, and

the total spectral content is simply referred to as Cho. Choline has been found to have a

significant increase (P < 0.01) in both low and high grade brain tumors as compared to healthy

controls35.

25

Lactate is the end product of anaerobic glycolysis and is increased when normal

metabolism is disrupted40. The most prominent resonance of lactate is from the methyl group

(CH3) J-coupled to the methine group (CH). This J-coupling causes a splitting of the three

protons into a doublet located at 1.3 ppm. The CH group produces a quartet at 4.1 ppm which

is not typically quantified, due to its proximity to the very large resonance from water in

comparison. The CH2 chain of lipids also resonates at approximately 1.3 ppm, thus lactate is

typically measured at an echo time of 1/2J (144 ms) when the doublet is in phase but with

distinctive negative amplitude in relation to lipids. To quantify lactate accurately in the

presence of lipids, spectral editing techniques or 2D spectroscopy must be used. Due to the

very small concentration of lactate in the healthy brain, coupled with the overlap of lipids, this

metabolite is detected at elevated levels only under numerous pathological conditions such as

stroke41, bipolar disorder42, cancer35, among others.

All five of these metabolites are disrupted by a variety of disorders such as brain

tumors43, stroke,41 Alzheimer’s disease44 and Schizophrenia45. Indeed, most brain pathologies

that influence brain biochemistry are likely to result in measurable MRS changes, and the

implications of these changes are an aspect of ongoing research. The MRS focus on these five

metabolites is not due to their greater biological importance than other chemical species within

the brain but due to relative ease of measurement.

Figure 1.6 displays the MRS result obtained from a voxel within parietal white matter of

a healthy volunteer. The four main metabolites are labelled, as well as glutamine/glutamate

(Glx). Lactate is not present due to its very small concentration in a healthy brain.

26

Figure 1.6: Spectrum obtained from parietal brain tissue of a healthy volunteer using PRESS

with TE = 30 ms echo time at 3 T. Metabolites Cr, glutamate/glutamine (Glx), mI, Cr, Cho, NAA

and lipids are labelled.

Beyond these five metabolites (NAA, Cr, Cho, Lac, mI) the remaining metabolites are

more reliably measured at 3 T by employing special spectral techniques, such as editing

(Section 1.2.4) or 2D MRS (Section 1.2.5), due to their relatively low metabolite concentration

and strong spectral overlap with other resonances. Some of these lesser metabolites are

studied in Chapter 4, and are briefly summarized below. Glutamate (Glu) and gamma-

Aminobutyric acid (GABA) are of particular interest due to their roles as the main excitatory and

inhibitory neurotransmitters, respectively46, as well as a variety of other functions32. Glutamate

and GABA typically reside in the synaptic vesicles29,46 within neurons but are released into the

synaptic cleft where they bind to postsynaptic receptors. Alanine (Ala) is a non-essential amino

acid used in protein synthesis and in the transfer of ammonia from astrocytes to neurons in the

glutamate/glutamine cycle47. Ascorbic acid (Asc), more commonly known as Vitamin C, is found

in measurable quantities within the brain and is a vital antioxidant. It also plays a role in

catecholamine synthesis, collagen production and regulation of the protein HIF-1α48. Aspartate

(Asp) is a neurotransmitter that plays a role in the termination of the signals from

27

neurotransmitters at the excitatory synapse49. Glutamine is a precursor of several amino acids

such as glutamate, aspartate and GABA. Glutamine also plays a role in the synthesis of the

messenger molecule nitric oxide (NO) by controlling the supply of the precursor of NO,

arginine50. Glycine (Gly) plays a significant role as one of the other major inhibitory

neurotransmitters51. Glutathione (GSH) is a tripeptide that plays a critical role in disposal of

peroxides by brain cells and in the protection from reactive oxygen species due to the high rate

of oxidative metabolism within the brain52. Glutathione is also an anti-oxidant that is essential

in maintaining healthy red blood cell structure27. Phosphorylethanolamine (PE) is the main

precursor of ethanolamine, which is an essential structural component of cell membranes and

is involved in regulatory roles such as cell division, activation, cell signaling, autophagy and

phagocytosis53. Scyllo-inositol is the second most abundant naturally occurring isomer of

inositol and acts as a main precursor to mI31. Taurine (Tau) is one of the most abundant amino

acids in the brain and has been shown to activate glycine receptors as well as an activator of

extrasynaptic GABAA receptors54.

1.3.3 Gliomas

Gliomas are characterized by uncontrolled growth of a variety of different cell types within the

brain. There are many different types of glioma neoplasms, each with their own respective

biology. The incidence rate within the United States for primary brain tumors is 18.1 per

100,000 person-years, with a 2-, 5-, 10- and 20-year observed survival rates of 62%, 54%, 45%

and 30%, respectively55.

Despite their underlying biological differences the clinical presentation, diagnostic

approach and initial treatment plan are similar across different glioma types56. Initial symptoms

are categorized into two different types: generalized or focal. Generalized symptoms are

usually the results of increased intracranial pressure (due to tumor growth) and include

headaches as well as nausea, vomiting and sixth-nerve palsy in severe cases. The location of the

tumor impacts the presentation focal symptoms which can include hemiparesis (partial

paralysis on one side of the body) or aphasia (speech deficit). Seizures also occur in 15 to 95

percent of patients and may be either focal or generalized56. Neural stem cells, progenitor

cells or de-differentiated mature neural cells can all mutate into gliomas57. Depending on the

28

cellularity, cytonuclear atypia, tumor differentiation, mitotic activity, microvascular

proliferation and degree of necrosis, gliomas can be further subdivided into four grades

established by the World Health Organization (WHO). Grades 1 and 2 are diffuse infiltrating

low-grade gliomas, Grade 3 are anaplastic gliomas and Grade 4 are glioblastomas, with

increasing grade indicating increasing aggressiveness57. Grades 1-3 are considered low-grade,

whereas Grade 4 is considered high grade. Over time Grade 2 and Grade 3 will progress to high-

grade gliomas, which are referred to as secondary glioblastomas. Glioblastomas are the most

deadly and common form of glioma.

Magnetic Resonance Imaging is the most common diagnostic test for patients

presenting with symptoms of brain cancer56. A typical MRI protocol for this application,

depending on the healthcare centre and time permitting, consists of a localizer (T2-weighted

fast spin echo), T2 fluid-attenuated inversion recovery sequence followed by a pre- and post-

contrast (gadolinium) T1-weighted spin echo sequence57, such as the clinical portion of the

protocol used in Chapter 3. Furthermore other magnetic resonance techniques may provide

complimentary information and may also be applied, such as tumor-cell density from diffusion

imaging or proliferation rate of tumor cells as well as necrosis (from the presence of lactate and

lipids) from MRS57. Additionally other medical imaging such as positron emission tomography

(PET) is also sometimes supplemented, especially in patients with presumed low-grade

gliomas56, which are characterized by glucose hypometabolism58.

Although relatively independent of glioma type, as mentioned above, the exact

treatment plan depends on the grade of the glioma. Treatment of direct symptoms involves

steroids to relieve edema, anticonvulsants in patients with seizures and antianxiety or

antidepressants for help with the psychological effects. For gliomas treatment includes surgical

resection of the tumor, as well as radiotherapy (60 Gy to the tumor) with or without the use of

chemotherapy agents such as temozolomide57. For grade 3 gliomas typical treatment includes

maximal possible surgery and radiotherapy (60 Gy). Low grade gliomas account for only about

25 % of diffuse gliomas and present as a non-contrast enhancing lesion on MRI. Typically

disease progression involves slow growth followed by a malignant transformation to a

glioblastoma that is the cause of death around 5 – 15 years after onset57. Retrospective studies

29

have shown that patients with an early and greater extent of resection have postponed

transformation to a secondary glioblastoma and improved survival59. Radiation is also a

standard treatment (50 – 54 Gy), but the optimal timing and delivery is an open topic of

research57, as the tumors are relatively benign for a long period of time.

It is well known that the rate of anaerobic glycolysis is markedly elevated in neoplastic

cells even in the presence of sufficient oxygen, and this phenomenon is referred to as the

Warburg effect60. Because of this, MRS has been used to differentiate between neoplastic and

nonneoplastic lesions due to the differences in metabolism43, and to differentiate between low

and high grade gliomas61. Extensive literature has investigated these prospects with MRS meta-

analysis62 showing the use of MRS to supplement anatomical MRI in the diagnosing of brain

tumors, exhibit sensitivity and specificity of 80 % and 78 %, respectively. Another meta-analysis

observed no observed statistical significance in the accuracy of assessing tumor recurrence

between MRS and PET63. This is an important result as PET is recognized to be sensitive to

radiolabeled compounds at much lower concentrations (~nM) than MRS, which detects

metabolites at ~mM concentration at 3 T.

Compared to healthy tissue, neoplastic lesions have decreased NAA concentration due

to neuronal breakdown, increased Cho due to increased cellular turnover, increased levels of

mI due to increased number of glial cells, a large increase in lactate due to anaerobic glycolysis,

and increased lipids due to necrosis and membrane breakdown43. In addition, MRS provides a

measurement of 2-hydroxyglutarate64 which is only present in glioblastomas that have the

genetic mutations IDH1 or IDH265.Thus, MRS potentially provides useful information as a

diagnostic and prognostic biomarker in this context, as IDH1 and IDH2 confer improved

prognosis when compared to wild-type IDH65.

At present, it is very difficult to evaluate the efficacy of radiation therapy as outcomes

depend on the underlying pathology. Typical treatment responses are observable by solid

tumor changes on anatomical MR images after 6 to 8 weeks66. There are also short term and

long term side effects of radiation therapy. Short term side effects are generally mild and

include vomiting, tinnitus (ringing of the ears), alopecia (loss of hair) and skin changes, resolving

30

after the cessation of treatment. Long term side effects are more detrimental and include brain

necrosis, demyelination, calcifications, hearing loss and, due to the large radiation dose

involved, the potential for new tumors67. It is desirable to monitor response to treatment as

early as possible; in order to change treatment course and, if necessary, in non-responding

cases, avoid unnecessary harmful and expensive treatments. There are numerous techniques

which aim to detect treatment response before anatomical MRI changes. Three of the most

promising are diffusion-weighted MRI66, which measures microstructural changes, and chemical

exchange saturation transfer imaging68 and MRS69, which both measure changes in metabolism.

The application of MRS to early radiation response is currently limited by inherently low SNR

per unit time of the available methods. The aim of this thesis was to develop techniques which

can combat the issue of low SNR, improving clinical MRS, as well as develop new techniques

which could be used for early radiation treatment response. Within Section 5.4 a new study is

proposed which aims to utilize the techniques developed here to provide earlier detection of

treatment response, thereby potentially saving patients from harmful and unnecessary

radiation exposure.

1.3.4 Other brain lesions

Differentiating between tumor recurrence and radiation-induced necrosis is an ongoing issue in

neuro-oncology. Their appearance from diagnostic imaging and clinical symptoms are typically

similar, but their treatment course and outcome is different. Even specialized sequences are

not sufficient in some cases, as typically radiation necrosis is marked by elevated ADC on

diffusion-weighted imaging and low choline from MRS as compared to tumor recurrence but

both techniques yield results with substantial overlap, yielding in some cases false positive or

negatives70. The development of novel non-invasive diagnostic techniques to differentiate

between these two types of lesions is an active field of ongoing research which could have large

ramifications for those undergoing radiation treatment.

Although MRS has been shown to be useful in differentiating neoplastic and

nonneoplastic lesions another ongoing challenge is the differentiation of primary gliomas with

brain metastases, which may require different courses of treatment. Both typically exhibit

decreased NAA/Cr, increased Cho/Cr and a notable increase in lactate and lipids in the 1.3 ppm

31

region71 or an increase in ADC in diffusion-weighted images, as well as similarities in both T1

and T2-weighted anatomical MRI scans. Despite their similarities on currently available

diagnostic tests the prognosis and treatment is often quite dissimilar, thus it is clear that

improved diagnostic techniques could have large ramifications on the treatment of both

patients with metastases and gliomas.

1.3.5 Spatial Localization of in vivo MRS

There are two different categories of MRS: single voxel spectroscopy (SVS), which measures

only one coarse voxel; and magnetic resonance spectroscopic imaging (MRSI), which measures

many spectra simultaneously over a coarse Cartesian grid of voxels. The SVS category will be

explained more thoroughly as it is used preferentially in the thesis. To localize a coarse voxel,

SVS applies three orthogonal gradients during three frequency-selective RF pulses. The effect of

applying a gradient during an RF pulse is most easily understood from the small tip angle

approximation of the Bloch equations. Under this approximation72 and ignoring relaxation

(especially the change in longitudinal magnetization from RF pulses) it can be shown that the

resulting transverse magnetization profile is73

( ) ( )∫ ( ) ( )

(1.36)

This equation shows that the transverse magnetization as a function of z can be expressed as

the Fourier transform of the applied RF pulse waveform when in the presence of a gradient

applied along the z axis. To excite a “slice” of magnetization, mathematically expressed as a rect

function, the appropriate RF pulse waveform is a sinc function. This type of excitation is

desirable in many MRI applications as it simplifies spatial encoding of magnetization, defining

the “through-plane” resolution and requiring subsequent encoding in the remaining two spatial

dimensions within-plane. Equation 1.36 is used in Chapter 2, where two bands of

magnetization (instead of one) are excited by amplitude-modulating the excitation pulse by a

cosine function.

More generally, the relationship between the magnetization profile and the applied RF pulse is

obtainable by solving the Bloch equations using the Shinnar-Le Roux (SLR) transform method,

32

which is applicable in the small-tip as well as large-tip regimes74,75. Other specialized solutions

are possible, such as adiabatic pulses76,77, as used in Chapter 4, and RF pulse design continues

to be an important area of research. Irrespective of the precise nature of the RF pulse, spatial

localization is achieved in MRS through the subsequent application of RF pulses played out in

the presence of three orthogonal gradients, followed by large gradients referred to as

“crushers” which de-phase magnetization and eliminate any echo pathway outside the voxel78.

The area under the crusher gradients is carefully balanced to ensure that the echo pathway of

magnetization within the voxel, which experiences all three RF pulses, remains unaffected.

Figure 1.7 is the pulse sequence diagram for a typical PRESS experiment. In general, this pulse

sequence is repeated many times and the acquired signal from each repetition is averaged to

improve SNR. Figure 1.8 is a diagram depicting how spatial localization is achieved in most in

vivo SVS experiments. A magnetic field gradient, or “gradient”, is depicted as a function of time

and is a linear spatially-varying field term added to the main magnetic field

( ) ( ) (1.37)

where ( ) ( ) is the gradient vector, which is typically depicted by three

separate lines in a pulse sequence diagram, such as in Figure 1.8.

The most commonly used MRSI technique maps out spatial frequency using phase-

encoding gradients (referred to as “k-space”) to obtain spectroscopic data throughout the field

of view (FOV)79,80. Ignoring relaxation, the measured signal for a single coil can be expressed as

( ) ∫ ( ( ) ( )) ( )

(1.38)

where is the k-space vector, ( ) mapped out by the magnetic field gradient function,

i.e.

( ) ∫ ( )

(1.39)

33

( ) ∫ ( )

(1.40)

( ) ∫ ( )

(1.41)

Figure 1.7: Pulse sequence diagram for PRESS. Three orthogonal slices of magnetization are

excited by pulses , and . The crusher scheme dephases all magnetization outside

of the intersection of the three orthogonal slices, allowing the acquisition of a spin echo signal

from a single localized region, as shown in Figure 1.8. The data acquisition (DAQ) begins at the

spin echo maximum. The echo time is when the spin echo is fully rephased, and is equal to

twice the spacing between the two refocusing pulses. In principle the excitation pulse does not

34

need to be 90°, nor the refocusing pulses need not be 180°, however for a single shot this

produces the maximum signal.

Figure 1.8: Spatial localization obtained from the pulse sequence shown in Figure 1.7. Three

orthogonal bands of magnetization are excited and crushers are used to de-phase excited

magnetization outside the voxel. Spatial localization is identical for the STEAM pulse sequence,

except the RF pulses are each and a different set of crushers is used to select the

appropriate echo pathway.

This four-dimensional space can then be mapped out by applying a different combination of

gradients in the three orthogonal directions for each successive excitation (referred to as

“phase-encoding”). The result of taking the 4D Fourier transform of this data is a 4D space

where three of the dimensions are the position in each of the three orthogonal directions (as

prescribed by the phase-encoding gradients) and the fourth dimension is the associated

spectrum at each location. Due to time constraints typically only a two dimensional image is

encoded with k-space and the third spatial direction (usually the axial direction) is localized

using a slice-select RF pulse. Both MRSI and SVS are widely applied in humans81–83 to measure

35

metabolic changes, aid in diagnosis of various cancers and to monitor therapeutic response.

Typical MRSI scan times are long, however, and spatial resolution suffers from an inherently

broad sinc point spread function. The SVS method has improved SNR per unit time compared to

MRSI and when a single localized region is of interest, SVS is usually the preferable option. For

this reason, SVS is used exclusively throughout the thesis.

1.4 Parallel Imaging

Multi-channel receiver coils are an important feature of modern MR systems. Each individual

coil has its own spatial sensitivity for measuring magnetization at a particular position in space.

Each coil can be used to produce its own separate image and the individual images can then be

combined to produce a single image84, with an increase in overall SNR when compared to

imaging with a single large coil. The SNR increase is achieved because each individual coil is

designed with spatial sensitivity for a small fraction of the intended imaging volume, and thus is

only sensitive to noise sources arising from this fraction. In contrast, single channel coils are

designed for sensitivity to the entire imaging volume and all the associated noise sources.

The SNR benefits provided by multi-channel coils can also be used to speed up MRI. A

technique referred to as “parallel imaging” uses the signals from each coil creatively so that the

sampling distance between k-space lines can be increased when traversing k-space. This

decreases the acquisition time (fewer k-space lines are required), but also decreases the FOV

because the Nyquist sampling criterion is no longer satisfied over the entire imaging volume (ie.

high spatial frequency information will now masquerade as low frequency spatial information).

Aliased images with a characteristic “overlapping artifact” are formed if an inverse 2D Fourier

transform is used on undersampled raw k-space data. Several techniques have been developed

to disentangle the aliased images based on multi-channel coil information; the two most

common are generalized autocalibrating partially parallel acquisitions (GRAPPA)85 computed in

k-space, and sensitivity encoding (SENSE)86 computed in image space. These techniques use the

inherent spatially limited sensitivity of the receiver coils to estimate the data for the absent k-

space lines to reconstruct the unaliased image.

36

This thesis adopts SENSE reconstruction, as summarized below. The most common

SENSE image reconstruction is the weak reconstruction with SNR optimization. The unfolding

matrix, , used to disentangle the aliased images, is expressed as

( ) (1.42)

where the superscript indicates Hermitian conjugate, is the coil sensitivity matrix (coil

sensitivity for each voxel), and is the receiver noise covariance matrix (the covariance

between the noise from each of the coils). The disentangled signals, , are then obtained by

(1.43)

where is the measured signal. The SENSE reconstruction results in increased noise because

the coils are not perfectly uncoupled (the coils exhibit correlations between both MR signals

and noise) and because less k-space data are used for spatial encoding, such that

√

(1.44)

where is the SNR of an image reconstructed using SENSE, is the SNR of an

image from fully sampled k-space, g is the geometry factor or “g-factor” of the multi-channel

coils, and is the reduction factor by which k-space measurement is reduced. Using the

definition of the g-factor, the noise amplification due to the condition of the coil sensitivity

matrix86 is

( ) √( ) ( )

(1.45)

where n is the index for the nth reconstructed voxel.

The parallel imaging formalism described above was initially developed for imaging, however it

has since been used to speed up MRSI87,88. Previously, a novel technique for functional MR

imaging (fMRI) of brain activity was also developed, which used SENSE to select a few coarse

voxels89,90, instead of the more traditional method of acquiring an entire brain image. The

37

technique used cosine modulation of the RF excitation pulses (thus exciting two slabs of

magnetization instead of one), and coil sensitivity to reconstruct the signal from the

simultaneously excited voxels. This technique, however, could alternatively be used to speed up

MRS acquisitions instead of fMRI acquisitions. This idea is pursued in Chapter 2 of the thesis.

1.5 Absolute Quantitative MRS

Beyond qualitative analyses of MR spectra, quantitative MRS seeks to extract metabolite values

that are proportional to concentration by fitting the acquired spectra to a basis set containing

the quantum-mechanically simulated signal from all the different metabolites within the

spectrum. Several such packages exist such as LCModel91, jMRUI92, AQSES93, TARQUIN94 and

ProFit14,15. Often the estimated concentration values are expressed as ratios to Cr, as it is

considered the most stable metabolite. It has been previously shown, however, that implicitly

assuming Cr to be stable can confound the quantitative analysis based on ratios36, as Cr levels

can also be strongly affected by disease. Furthermore, the ratios depend on the concentration,

T1 and T2 of both the metabolite of interest and creatine. Thus, changes in the ratio could be

due to a variety of factors, and in some cases the ratio may not change when the underlying

values do. For this reason, it is desirable to estimate the physical concentration of these

metabolites (not just ratios), which is referred to as quantitative MRS.

It can be shown that for a particular chemical species, the magnetization measured with PRESS

is given by

(

)

[ (

) (

) (

)]

(1.46)

where is the equilibrium magnetization for the particular chemical species and TR is the

repetition time. Thus to obtain estimates of , which is proportional to physical

concentration, the measured signal must be multiplied by a correction factor:

38

(

)

[ (

) (

) ( )]

(1.47)

In quantitative MRS the values obtained from the fitting software are corrected for relaxation

and then scaled to represent actual physical concentrations. The two most common ways to

scale to absolute concentration are using an internal35,64 or external95,96 reference. When an

internal reference is used, the measured signal from a certain chemical species (typically water)

is set to be a concentration that is previously known from the scientific literature. When an

external reference is used, the known concentration is typically taken from a phantom that is

measured using MRS before or after in vivo MRS measurements are made. In both cases, the

measured reference signal must also be corrected by Equation 1.47 using the appropriate

relaxation values prior to scaling and obtaining quantitative MRS data.

1.6 Diffusion-weighted MRS

By applying a series of refocused gradients, it is possible to make proton MR signals attenuate

in a manner that is sensitive to diffusion7,97,98. The attenuation is a result of spins diffusing in-

between successive gradients, resulting in a slight phase accrual upon refocusing. This phase

accrual integrated over many spins results in a reduction in the measured signal dependent on

the amplitude of the amplitude ( ) and duration of the applied gradients ( ), the time between

refocusing gradients ( ) as well as the rate of diffusion of the spins. The diffusion

characteristics of spins are highly sensitive to tissue geometry and morphometry99.

Water is the molecule of interest in diffusion-weighted MRI (DW-MRI). The basis of

diffusion-weighted MRS (DW-MRS) is identical to DW-MRI, except that the molecules of

interest are now metabolites (typically NAA, Cr and Cho), and not water. The SNR is thus

substantially decreased for DW-MRS in comparison to DW-MRI, and this has meant that SVS

approaches have been adopted. However, DW-MRS has recently been extended to multiple

voxels using the MRSI technique100 (as explained in Section 1.3.5). The most basic of DW-MRS

experiments involves measuring the spectra twice: once using large applied diffusion-sensitizing

gradients (DSGs) and once using the identical pulse sequence with negligible DSG amplitude.

39

The apparent diffusion coefficient, ADC, of the metabolite can then be estimated through the

equation98

( )

( )

(1.48)

where is the value estimated for a particular metabolite by fitting the measured signal

obtained with negligible DSGs, and is the corresponding value with the DSG amplitude set to

. The spin accrues a phase , in the presence of gradients according to

∫ ( ) ( )

(1.49)

where is the position vector of the spin as a function of time. By Taylor series expanding the

position vector and ignoring the terms above linear motion it can be shown that the phase

accrued due to a bipolar gradient is

( ) (1.50)

where is the angle between the applied gradient direction and the motion and v is the

average velocity of the spin. Using Equation 1.50 and typical values for diffusion-sensitizing

gradients (as given in Chapter 4) a velocity of approximately 1 mm/s in the same direction as

the applied gradient results in a full phase accrual, which is approximately the macroscopic

motion of the head during in vivo experiments. It is therefore paramount to re-phase the

individual excitations prior to averaging due to the interaction with motion and the large DSGs

resulting in large phase variations from one TR to the next101. Figure 1.9 is a diagrammatic

explanation of the need for re-phasing MRS data prior to summing over successive excitations

in a healthy volunteer.

40

Figure 1.9: The effect of the order of summing and phasing the individual excitations in DW-

MRS from in vivo healthy volunteer data with 64 individual excitations. The vertical scales for

each row are constant. The measured amplitude of the metabolites is drastically reduced when

the excitations are summed prior to phasing.

In the presence of non-linear motion, Equation 1.50 is no longer valid and DW-MRS

signals suffer further attenuation artifact. Because of this, cardiac gating is usually necessary in

DW-MRS to limit the effects of non-linear motion from cardiac pulsatility101 in the brain. Failure

to correct for motion by re-phasing and cardiac gating will result in substantially reduced values

when measuring MRS signals at high-b values, producing ADC estimates that are artificially

high.

Diffusion-weighted MRS offers unique intracellular information as the metabolites

exhibit much more restricted diffusion than water, with correspondingly lower ADC values101–

109. The DW-MRS experiment is usually limited to investigating NAA, Cr and Cho, largely due to

the relatively high SNR and ease of measuring these metabolites in relation to the others that

have weaker single strength. Thus, there is a need to improve on the current capabilities of DW-

MRS, so that signals from other metabolites can be measured reliably.

41

1.7 Hypotheses and Thesis Outline

This thesis focuses on the technical development and application of three novel in vivo MRS

pulse sequences. Ultimately these sequences may be applied in a variety of different

applications, although their motivation was ultimately towards using MRS as a predictor for

early treatment response to radiation therapy, as discussed further in Section 5.4.

Chapter 2 tests the hypothesis that by using tailored RF excitation and SENSE parallel

imaging methods, it is possible to obtain high quality MRS data from two voxels simultaneously

without the need for a full k-space encoding procedure, as is typically done in MRSI. This new

approach should offer all the benefits of SVS over MRSI, such as shorter scan times and lack of

point spread function effects on spatial resolution, while allowing the simultaneous

measurement of both voxels. The method is demonstrated in phantoms, healthy controls and

patients with brain cancer. For this chapter I implemented the developed pulse sequence,

performed all experiments on phantoms, healthy volunteers and patients, and analyzed all data

using a combination of custom scripts and a quantitative MRS software package.

Chapter 3 develops a novel inversion recovery sequence which is then combined with

spectral editing to measure the longitudinal relaxation time of lactate in a cohort of glioma

patients. This technique enables T1 to be estimated with improved precision compared to the

use of standard inversion recovery for a fixed experiment time. This novel sequence is then

used to test the hypothesis that lactate has a significantly different T1 relaxation value than the

contaminating lipids in patients with brain cancer. The lactate T1 value is then used to obtain

estimates of absolute metabolite concentration and to optimize the TR value in MRS

experiments involving this metabolite. For this chapter I developed the concept for the pulse

sequence as well as identified the gap in the literature, in addition to writing the pulse

sequence, performing all experiments and analyzing all data using a combination of custom

scripts and a quantitative MRS software package.

Chapter 4 develops a novel technique that combines DW-MRS and JPRESS. This

sequence is then used to investigate what metabolites beyond NAA, Cr and Cho can have their

diffusion coefficients reliably estimated at 3 Tesla in healthy volunteers. For this chapter I

42

developed the concept for the pulse sequence, in addition to writing the pulse sequence,

performing all experiments and analyzing all data using a combination of custom scripts and a

quantitative MRS software package, with the help of Rofl Schulte and Ben Geraghty to

implement the software package.

Lastly, Chapter 5 provides the overall conclusions of the thesis and discusses work that

could be done in the future to extend the research, including technical improvements and

potential applications of the sequences developed here.

43

Chapter 2

Constrained Source Space MR Spectroscopy:

Multiple Voxels, No Gradient Readout

A paper published in American Journal of Neuroradiology, 2015, pp 1-8 by Karl Landheer, Arjun

Sahgal, Sunit Das and Simon J. Graham.

2.1 Introduction

There are two major categories of magnetic resonance spectroscopy (MRS) pulse sequences on

current clinical MRI systems: single voxel spectroscopy (SVS), which measures one voxel; and

magnetic resonance spectroscopic imaging (MRSI), which measures many spectra

simultaneously over a Cartesian grid of voxels. Both SVS and MRSI are widely applied in humans

to detect certain molecular constituents of normal and abnormal tissues, especially those

associated with cellular metabolism, and to monitor therapeutic response81–83. Each MRS

category has its application niche, as SVS and MRSI exploit different spatial and temporal

resolution trade-offs. SVS is attractive when anatomical MRI provides precise indication of

where spectral information should be collected. When pathology is more diffuse, widely

distributed, or not detectable on anatomical MRI, MRSI is the technique of choice to generate

spectra from many voxels using multiple repetitions for k-space encoding79,80. To reduce

spectroscopic scan times various "parallel imaging" approaches have been applied to reduce

the amount of k-space data acquired. These techniques exploit the spatial sensitivity of

individual elements in multi-channel receiver coils87,88,110,111 and can substantially reducing scan

times.

The spatial limitations of SVS are well recognized; it is usually the case that SVS spectra

are required at more than one location, either to compare spectra from diseased and normal

44

tissue, or in the case of multi-focal disease. This naturally leads to execution of SVS pulse

sequences successively for each voxel location. There have been some attempts to modify

spectroscopy acquisition to extend the volume of coverage of SVS, such as line scan echo planar

spectroscopic imaging112 which provides spectra from a column of voxels. However, for clinical

applications, standard SVS methods, notably point resolved spectroscopy (PRESS)4 and

stimulated echo acquisition mode(STEAM)113 remain entrenched.

Previously, a technique was developed that uses RF localization and sensitivity encoding

(SENSE)86 for fast functional magnetic resonance imaging90. It is reasonable that this approach,

appropriately modified for MRS applications, should be investigated more to determine

whether it usefully augments existing SVS capabilities. In the present work, referred to as

constrained source space magnetic resonance spectroscopy (CSSMRS), a prototype pulse

sequence is developed and analyzed for its ability to acquire and separate spectra from two

voxels simultaneously with no k-space encoding. The efficacy of spectral separation is

investigated for a variety of distances between the two voxels in a healthy volunteer.

Additionally, numerical simulations are preformed to assess the validity of certain assumptions

made in the reconstruction and to predict CSSMRS performance in cases where lengthy

experimentation is impractical. Lastly, two-voxel CSSMRS data are reported in relation to

conventional SVS data acquired successively at each voxel location for patients with a variety of

different brain cancers ranging from low grade to high grade.

2.2 Materials and Methods

All experimental data were collected using a GE 750MR 3.0T MRI system (General Electric

Healthcare, Waukesha WI) with a standard 8-channel head coil receiver. To achieve CSSMRS

for proof-of-principle demonstrations, a standard PRESS sequence was modified to excite two

voxels arbitrarily in space, instead of one (Figure 2.1a). Illustrative voxel locations are shown

overlaid on the anatomical image of a patient with brain cancer in Figure 2.1b (see Patient 6 in

Table 2.1 below). The two user inputs were the voxel size, chosen throughout as (20 mm)3; and

the x, y, z coordinates of each voxel location. In this approach, two arbitrarily positioned voxels

were excited via cosine modulation of the first RF pulse, which resulted in the excitation of two

45

parallel slices, followed by the standard spin echo formation process thereafter. Arbitrarily

localization is obtained by modifying the offset frequencies of the RF pulses and changing the

rotation array between logical and physical gradients. The three RF pulses were Shinnar-Le

Roux pulses75 with durations of 3600, 5200 and 5200 ms and bandwidths of 2366.67, 1384.62

and 1384.62 Hz for the first, second and third pulse, respectively.

The additional pulse sequence parameters for this initial work included a repetition time

(TR) and echo time (TE) of 1500 ms and 288 ms (unless otherwise stated), respectively; a flip

angle of 63° (approximately the Ernst angle); a readout bandwidth of 2500 Hz; and 1024 points

data acquisition (total acquisition time of 409.6 ms). The value TE = 288 ms was chosen because

it has been shown to have high MRS reproducibility114, an important clinical factor compared to

the other common TE values of 30 and 144 ms, despite the associated reduction in signal-to-

noise ratio (SNR). Water suppression was implemented using chemical shift selective

saturation19. Prior to all data acquisitions, 1st and 2nd order shimming was applied

encompassing most of the brain to decrease spectral linewidths. The typical linewidth of the

water peak was about 8 Hz. The total number of excitations was 128, with a total scan time of

3.2 minutes. The reconstruction of CSSMRS requires a calibration scan to measure the coil

sensitivity, which is explained below.

Regarding spatial reconstruction of CSSMRS data to separate spectra from the two

voxels, the governing equation can be expressed in matrix form as86,90

( ) ( ) ( ) (2.1)

where the sensitivity matrix relates how the magnetization signals ( ) from each voxel result

in the acquired signals ( ) from each element in the receiver coil, and ( ) represents coil

element-dependent noise. The sensitivity matrix is generated by assuming that the spatial

sensitivity of each coil element varies slowly over the extent of each voxel. For each of the

coils and slices,

46

∑ ∑ ( )

(2.2)

where is the number of pixels within the specified region inside the voxel, and are

the respective minimum and maximum row pixel limits on the voxel and and are the

respective minimum and maximum column pixel limits on the voxel. Equation 2.1 can be

solved by the SENSE formalism using weak reconstruction with SNR optimization86:

Figure 2.1: a) Pulse diagram for CSSMRS. The first RF pulse has a flip angle of α (where α < 90°)

and is cosine-modulated, such that the subsequent spin echo after the third RF pulse excites

two coarse voxels. Shaded gradients are crusher gradients. The slice-select rephasing lobe for

the y gradient is added directly to the first crusher. The gradient echo readout in the dotted box

is optional for voxel localization verification. See text for further details. b) An anatomical T1-

weighted image of patient 6 with the nominal voxel locations overlaid, and a brain tumor

evident in the left middle temporal gyrus. The two spectra for this patient are displayed in the

bottom row of Figure 2.2.

a)

b)

47

( ) ( ) ( ) (2.3)

where is the estimated magnetization signal for each voxel, denotes the pth repetition

and represents the noise covariance matrix between the coils. For example,

∑(

( ) )( ( ) )

(2.4)

where the complex noise samples can be taken from the last datum of each acquisition, and the

covariance is calculated over the total number of excitations,

The matrices can be estimated by various approaches 115–117 although previous CSS

work has shown that a simple procedure is sufficient for proof-of-concept implementation90.

Two sets of fast gradient echo (FGRE) images were acquired with the same pulse sequence

parameters (TE/TR = 1.3/34 ms, flip angle = 5°, field of view = 30 cm, 64 by 64 acquisition

matrix, 5 mm slice thickness): one set with the body coil, and one set with the multi-channel

head coil receiver. These images were then spectrally interpolated to produce 256 by 256

images with an isotropic in-plane resolution of 1.17 mm. For each of the coils and slices, the

coil sensitivity map at each in-plane coordinate, ( ), was estimated by dividing each

of the individual head coil images by the analogous body coil image, and then thresholding

using an “object indicator” to set the coil sensitivity to zero in regions where noise dominates

object signal.

The CSSMRS reconstruction was performed using specially-written scripts in MATLAB

(the Mathworks, Inc., Natick, MA). The two separated signals were first zero-filled by a factor of

two, then transformed to the spectral domain by fast Fourier transformation. The spectra were

then phase-corrected including zero and first order correction terms using an automated

algorithm based on minimizing entropy.118 The spectra were then shifted in frequency to place

the peak for N-Acetylaspartate (NAA) at 2.04 ppm; normalized by their L2-norm; and subjected

to Hankel Lanczos singular value decomposition93 for removal of residual spectral content

arising from water. Spectral components were then quantified automatically using the

48

Figure 2.2: Spectra from both a healthy volunteer (a and b) and a brain cancer patient (c and d)

measured with both CSSMRS and PRESS. Spectra from Patient 6 (See Table 2.1 for list of all

patients) are shown because this patient exhibited the median g-factor, typifying CSSMRS

reconstruction quality. Errors represent the standard deviation over 128 excitations. a.u. =

arbitrary units.

freeware “SPID” which utilizes a separable nonlinear least-squares fitting algorithm known as

automated quantitation of short echo time MRS spectra (AQSES).93 The AQSES algorithm

provides Cramer-Rao lower bound estimates of the standard deviation of each quantified

spectral component. The basis set used was simulated using Java Magnetic Resonance User

Interface (jMRUI) and the input scan parameters. The values obtained from the quantification

algorithm for NAA, Cho and Cr were then scaled by attenuation factors to account for

transverse and longitudinal relaxation effects using relaxation constants obtained in a normal

brain119. The values for lactate were not adjusted for attenuation according to common

practice.

Bloch equation simulations confirmed that cosine modulation had negligible effect on

the integrity of the spatial profile. A water/fat phantom was used to measure the bleed

49

between voxels. One voxel was placed inside a stationary fat container and another voxel was

placed inside the surrounding water bath. Typical scan parameters were used except an echo

time of 40 ms (for increased SNR) and a total number of excitations of 32. This scan was

repeated for centre-to-centre distances of 40.2 to 70.2 mm. The bleed was defined to be the

amplitude of the contaminating absorption spectrum divided by the amplitude of the main

absorption spectrum in the other voxel multiplied by 100%. Two validation experiments were

subsequently conducted on healthy volunteers and patients with brain cancer, to assess

CSSMRS capabilities in practical scenarios. All volunteers participated with free and informed

consent and with the approval of the hospital research ethics board.

Experiment One was performed to investigate how CSSMRS results are affected by voxel

placement in relation to coil sensitivity profiles. Because CSSMRS involves SENSE

reconstruction, overall performance depends on the condition number of the reconstruction

matrix, as quantified by the "g-factor"86:

( ) √( ) ( )

(2.5)

where the integer is used to denote the different voxels that are reconstructed (ie. [ ]

in this case). To assess CSSMRS results for various g-factors, one voxel was placed in a fixed

central location in the brain, and the other was placed to achieve centre-to-centre separations

between voxels varying from 20 mm (ie. adjacent voxels) to 70 mm in the radial direction

toward the head coil. SVS PRESS data were acquired in each successive location for

comparison. These CSSMRS and PRESS data were collected for one healthy young male adult

(23 years old). Equation 2.2 was then used to calculate the sensitivity matrix from the

measured coil sensitivities at each individual voxel location, which, along with the noise

covariance matrix (Equation 2.4) can be used to calculate the g-factor using Equation 2.5.

Experiment Two was performed to investigate how well CSSMRS distinguishes spectra

from cancerous and normal tissue over a representative range of clinical presentations. Six

patients with brain cancer were recruited from the Sunnybrook Odette Cancer Centre during

the course of their treatment (see Table 2.1 for tumor characteristics). Patients were included if

50

they presented with a tumor volume approximately the same size or larger than the prescribed

voxel. Tumor location was verified using a high resolution fast spoiled gradient echo with an

anatomical inversion recovery preparation (FSPGR IR prep, acquisition parameters given

below). For all patients, one voxel was placed at the centre of the tumor and the other was

placed on the contralateral side in the analogous neuroanatomical region within normal-

appearing brain tissue. PRESS data were also acquired successively in these two locations for

comparison purposes.

In both experiments, PRESS was performed with the identical acquisition parameters

used in CSSMRS and with the same spectral analysis pipeline. The total examination time for

comparing CSSMRS and PRESS data from two voxels was approximately 20 minutes, which

included scout images, anatomical MRI (FSPGR IR, 256 by 256 pixels, pixel size = 0.86 mm by

0.86 mm, TR/TE = 8.2/3.2 ms, flip angle = 8°), two FGRE scans (for measuring coil sensitivity as

described above), higher order shim, CSSMRS and PRESS acquisitions.

Table 2.1: Summary of brain tumor patients studied in Experiment Two.

Patient Age (years)

Sex Disease Radiation Treatment Status

Tumor Location Tumor Size (vs Voxel Size)

1 36 F grade II oligodendroglioma

none right cingulate gyrus

Larger

2 84 M grade IV glioblastoma

currently undergoing focused radiation

left middle temporal gyrus

Comparable



left superior temporal gyrus

Larger

4 61 F brain metastases from breast cancer

60 days since completion of focused radiation


Smaller

5 79 M brain metastases from colon cancer

70 days since completion of focused radiation

right superior temporal gyrus

Smaller




Larger

51

A simple numerical simulation was also written in MATLAB for additional insight into the

results of Experiments One and Two. The simulation assessed the impact on spatial

reconstruction of the important assumption underlying Equation 2.1, namely that coil

sensitivity could be reasonably approximated as a constant over each voxel. Given good

agreement between experimental results and simulations for Experiment One (see Results) the

simulation also was used to predict CSSMRS performance under conditions that were not

possible to measure experimentally during Experiment Two, due the inherent time restrictions

for collecting MRS data in patients.

The simulation used measured coil sensitivity data and PRESS data from two voxels as

initial inputs. In the context of the simulation, the PRESS data (obtained according to

experimental parameters given above, averaged over 128 excitations) were considered to

represent a situation in which signal components were uniformly concentrated over each voxel

volume. Simulated signals were then generated for each coil element while accounting for non-

uniform coil sensitivity, by performing the appropriate spatial integral. Complex Gaussian noise

was added to each simulated signal to approximate the levels observed experimentally for each

coil. These simulated coil signals were then used for spatial reconstruction of two voxel signals

according to the Equations 2.1 – 2.4 above, for subsequent comparison with the PRESS data

that were originally input. Spatial reconstruction, spectral processing and analysis were

conducted identically to the procedures outlined above for experimental data.

2.3 Results

For centre-to-centre spacings of 40.2, 50.2, 60.2, 70.2 mm the observed bleed of water into the

fat voxel was 2.0%, 1.4%, 1.5%, 1.7% and 1.3%, respectively, and the observed bleed of fat into

the water voxel was 3.7%, 4.7%, 4.0%, 3.0%, 0.7%, respectively.

For visual comparison, Figure 2.2 displays four representative spectra obtained by

CSSMRS (solid black line) and PRESS (dashed grey line), respectively. As commonly performed

for display purposes, all spectra were apodized by a Gaussian filter with 2 Hz full-width-at-half-

maximum. The spectra shown in Figure 2.2a and Figure 2.2b are qualitatively similar and were

obtained from a healthy volunteer with both voxels placed inside the prefrontal cortex. The

52

spectra shown in Figure 2.2c and Figure 2.2d were obtained from Patient 6 (see Table 2.1) and

are substantially different for the two voxels, with the spectra in Figure 2.2c obtained from

tumor tissue inside the left middle temporal gyrus and those in Figure 2.2d obtained from

contralateral homologous tissue, as shown in Figure 2.1b. Spectra from Patient 6 were chosen

for display in Figure 2.2 because CSSMRS results were obtained in this case with the median g-

factor observed over the patient cohort. Figure 2.3 displays spectra obtained from both

CSSMRS and PRESS for the minimum achievable echo time of this pulse sequence (30 ms).

Figure 2.4 shows the tumor spectrum obtained from CSSMRS for patient 1 and the fit obtained

from AQSES.

The results of Experiment One and related numerical simulations are shown in Figure

2.5, which plots the difference between quantified spectral components measured by CSSMRS

and PRESS for six different voxel separations (one voxel held fixed, one moved radially) and the

three main metabolites observed in Figure 2.2a and Figure 2.2b: NAA, creatine (Cr) and choline

(Cho). The difference values (CSSMRS minus PRESS) reported are specifically for the voxel that

was maintained in a fixed position. For both the experimental and simulated results, the

difference between CSSMRS and PRESS remained constant within error over all voxel

separations. Furthermore, the difference values for experimental and simulation results also

agreed within error, with the only exception being a slight bias in NAA quantification when

voxels were placed adjacent to one another (20 mm separation distance).

Given the good level of agreement between experiment and simulation observed in

Figure 2.5, numerical simulations were then extended to assess CSSMRS reconstruction quality

as a function of voxel separation with spectra that were substantially different in the two

voxels. Figure 2.6 plots the difference between quantified spectral components measured by

CSSMRS and PRESS in a manner analogous to that shown in Figure 2.5, however in this case the

inputs to the simulation were provided from Patient 6 with the difference values relating to

quantification of the tumor spectral components: NAA, Cho, Cr, and lactate (Lac). For

additional context, the difference values obtained experimentally for Patient 6 are also

indicated as single data points in Figure 2.6. Similar to Figure 2.5, Figure 2.6 shows difference

values of zero within error for all voxel separations and metabolites except choline for the first

53

Figure 2.3: Spectra from a healthy volunteer at 30-milisecond echo time, obtained by using

both CSSMRS and PRESS. The labeled metabolites are myo-inositol (mI), Cho, Cr, Glx, and NAA.

Figure 2.4: The unapodized spectrum obtained from CSSMRS from patient 1 (highest g-factor)

along with the automated quantitation of short echo time MR spectroscopy spectra (AQSES) fit.

54

two separations and NAA for adjacent voxels, indicating that good CSSMRS reconstruction

quality is maintained even when the two voxels are located in close proximity to one another.

Additionally the reconstruction tends to improve as the distance between the voxels increases

for all metabolites. The simulation results and experimental results also agree within error for

the single experimental data point.

Figure 2.5: Measured and simulated differences between the CSSMRS and PRESS measurement

for six different voxel separations for the three main metabolites within a healthy adult brain:

N-acetylaspartic acid (NAA), choline (Cho) and creatine (Cr). The signal from the CSSMRS coarse

voxel that was kept in fixed position was reconstructed and compared to the PRESS

measurement obtained from the same location. The black line and gray lines are the measured

and simulated values, respectively. The g-factors are also displayed for reference at the top x-

axis, although there is a non-linear relationship between g-factor and voxel displacement. Error

bars denote Cramer-Rao bounds. a.u. = arbitrary units.

Summarizing the results of Experiment Two, CSSMRS and PRESS results are quantified in Tables

2.2 – 2.5 for Patients 1-6 across tumor and normal tissue voxels for NAA, Cr, Cho, and Lac,

55

including the differences in spectral quantification. The CSSMRS g-factors for Patients 1-6 were

1.67, 1.01, 1.21, 1.23, 1.13 and 1.18, respectively; this indicates that there should be a SNR per

square root of unit time benefit for CSSMRS over PRESS in all cases except for Patient 1.

Overall, large decreases in NAA and increases in Lac and Cho were observed for tumor voxels in

relation to normal tissue voxels for CSSMRS and PRESS for most patients, consistent with

previous papers43. Tables 2.2 – 2.5 also show a large variability in the tumor spectra across

patients. A Mann-Whitney U test on the pooled values from all metabolites obtained from

CSSMRS versus PRESS yielded a p-value of 0.90, indicating no significant difference. It should be

mentioned that the bleed values estimated in a water-fat phantom may not be representative

of those obtained in vivo, due to the differences in linewidths in a phantom versus a human, in

addition to the bleed being estimated from water and fat and not from metabolites. There is no

evidence of significant voxel bleed in the in-vivo experiments, as no systematic increase in Lac

was observed in normal tissue CSSMRS voxels (Table 2.5), except that a large Lac value was

obtained from CSSMRS and PRESS spectra in healthy tissue for Patient 4. Voxel placement was

close to the scalp in this particular patient, which produced contaminating lipid signals that

were subsequently misinterpreted as Lac by the AQSES software. Thus, this specific result

should be discounted. In addition there is a significant increase observed for this patient in

both NAA and Cr in the tumor voxel from CSS. This is likely due to motion which exacerbated

bleed effects, as this particular patient had difficulty remaining still.

56

Figure 2.6: Simulated metabolite quantification values for seven different voxel separations for

the four main metabolites within the tumor spectra for Patient 6: NAA, Cho, Cr, and lactate

(Lac). The quantified values were from the stationary voxel placed within the tumor, and are

plotted in gray. The black data points located at 78 cm in each plot are the experimental results

for this patient, corresponding to the first difference column values listed in Tables 2.2 – 2.5 for

Patient 6. The estimated g-factors are also displayed at the top x-axis for reference, although

there is a non-linear relationship between g-factor and voxel displacement. a.u. = arbitrary

units.

57

Table 2.2: Quantified NAA values from PRESS and CSSMRS for both voxels in arbitrary units

(a.u.). The standard deviations are Cramer-Rao bounds.

Patient CSSMRS tumor voxel

PRESS tumor voxel

Difference CSSMRS healthy

voxel

PRESS healthy

voxel

Difference

1

2

3

4

5

6

Table 2.3: Quantified Cho values from PRESS and CSSMRS for both voxels in arbitrary units



PRESS tumor voxel


voxel

PRESS healthy

voxel

Difference

1

2

3

5

6

58

Table 2.4: Quantified Cr values from PRESS and CSSMRS for both voxels in arbitrary units (a.u.).

The standard deviations are Cramer-Rao bounds.


PRESS tumor voxel


voxel

PRESS healthy

voxel

Difference

1

2

3

4

5

6

Table 2.5: Quantified Lac values from PRESS and CSSMRS for both voxels in arbitrary units



PRESS Tumor voxel


voxel

PRESS healthy

voxel

Difference

1

2

3

4

5

6

* Lipid contamination from scalp mislabelled as Lac in healthy voxel.

59

2.4 Discussion

This work has introduced a prototype pulse sequence for CSSMRS, a novel spectroscopy

technique that measures spectra from multiple voxels simultaneously without the need for k-

space encoding. Instead, spatial encoding is achieved by multi-voxel RF selective excitation,

signal readouts from a multi-channel receiver coil, and SENSE14 reconstruction to separate the

signals from each voxel. The CSSMRS method is important from the perspective of SNR per

square root of acquisition time, potentially providing efficiency in comparison to the standard

clinical practice of performing successive SVS acquisitions at different voxel locations.

Careful experiments and simulations were undertaken to investigate the capabilities of

CSSMRS for simultaneous measurement of two voxels. In particular, considerable attention was

paid to whether CSSMRS provides adequate spatial localization in relation to the standard SVS

PRESS method. In a water/fat experiment it was shown that the bleed was on the order of 2-

5%, which is acceptable for spectroscopic applications. Experiments One and Two, conducted

in healthy volunteers and a diverse group of six brain tumor patients with four different types

of cancer, showed overall that CSSMRS and successive PRESS spectra agreed within

experimental error. Furthermore, CSSMRS spatial reconstruction was shown to be robust over

a range of voxel prescriptions (with one voxel held fixed and the voxel separation varied), by

both experiment and numerical simulation. Experiment and simulation were in agreement for

a healthy volunteer, indicating excellent reconstruction even when the two voxels were placed

adjacent to one another. The only additional feature of note in this regard was a slight,

systematic discrepancy between the simulated and measured NAA values observed in Figure

2.5 for all voxel separations. This feature is likely due to the relatively simplistic nature of the

simulations, which did not account for various experimental factors. However, given that the

overall level of agreement between experiment and simulation was very good, these factors

evidently have a small influence. The simulation therefore helps to support the assumption

made in CSSMRS reconstruction that coil sensitivity variations can be neglected within the

voxels.

60

The agreement between these experiments and the simulation provided rationale for

using the simulation further to predict CSSMRS capabilities in a brain cancer patient. As

expected, slightly larger variations were observed as a function of voxel separation in this case,

likely due to the larger spectral differences between the two voxels. However, with the

exception of choline quantification for very closely spaced voxels (20 and 30 mm centre-to-

centre) and NAA with adjacent voxels, all CSSMRS results were predicted to be consistent with

PRESS results within error.

Given that CCSMRS has been demonstrated to provide robust, high-quality results,

discussion can turn productively to the potential efficiency of this pulse sequence in terms of

SNR per square root of acquisition time. In the two-voxel implementation investigated in the

present work, spectra were obtained in half the time compared to successive application of

PRESS. The quality of CSSMRS results is potentially affected by noise amplification in the SENSE

reconstruction, however, as parameterized by the g-factor. Therefore, the appropriate context

for using CSSMRS advantageously over PRESS is when the g-factor is less than √ . This

corresponds to a minimum centre-to-centre separation in voxels of about 55 mm near the

centre of the 8-channel head receiver coil used in this work. All but Patient 1 (who had a tumor

approaching the midline) had a g-factor below this threshold.

It is also interesting to note that CSSMRS is compatible with another approach that

avoids using k-space for spatially encoding spectral information. In principal, if the flip angles

assigned to each voxel can be modulated appropriately, then simple algebraic combinations of

the successive spectroscopic readouts can be used to localize each voxel without SENSE

reconstruction, as achieved in Hadamard Spectroscopic Imaging (HSI)24. The HSI approach is

independent of g-factor and also provides improvements in SNR per square root of time, but

has traditionally required excellent RF fidelity and is sensitive to how spatial RF nonuniformity

and patient motion influence algebraic combination and the subsequent leakage of signals

between voxels. In addition, the algebraic combination of multiple recordings reduces the

minimum temporal resolution that is achievable with HSI, whereas CSSMRS provides spectral

separation in as little as a single TR value. CSSMRS and HSI are not mutually exclusive,

61

however, and it is possible that a robust, hybrid technique can be developed in the future for

further scan time reductions.

Irrespective of developing such a hybrid technique, the present method has potential

applications in any in vivo spectroscopy experiment in which there are two regions are of

interest and the lengthy acquisition times of MRSI are impractical. CSSMRS could also be

beneficial in a research setting where sophisticated 2-dimensional MRS experiments have

inherently long acquisition times, such as JPRESS120. Another promising application of CSSMRS is

in functional spectroscopy where real-time changes in metabolic information could be

measured from multiple points within the brain simultaneously with high temporal resolution.

Further development and applications of CSSMRS will be explored in the future.

2.5 Conclusions

CSSMRS has been developed to extract signals from two localized regions

simultaneously and reliably. Utility was demonstrated in a clinical setting, although the

technique has promising applications in the research setting as well.

62

Chapter 3

A Rapid Inversion Technique for Measurement of

Longitudinal Relaxation Times of Brain Metabolites:

Application to Lactate in High Grade Gliomas at 3 T

A paper published in NMR in Biomedicine, 2016, vol. 29, pp 1381-1390 by Karl Landheer, Arjun

Sahgal, Sten Myrehaug, Albert P. Chen, Charles H. Cunningham and Simon J. Graham.

3.1 Introduction

Proton magnetic resonance spectroscopy (MRS) is a powerful non-invasive technique used to

measure biomarker activity within the brain and body. This technique has been used

extensively to investigate the biochemical profiles of brain tumors121. Typically, gliomas exhibit

a decrease in N-acetylaspartic acid (NAA) due to the degradation of neurons, and an increase in

choline (Cho) due to elevated cell density and membrane turnover in neoplasms. Especially

when necrosis is present, there is also often an accompanying increase in lipids. Lastly, a

significant increase in lactate is common, widely attributed to the increase in anaerobic

glycolysis40. Lactate is of particular interest due to its role in metabolism and its negative

correlation with survival time43.

For MRS results to be interpreted in detail, the magnetic resonance properties of each

spectral component must be well understood. One important property is the relaxation

time, which describes the timescale for longitudinal recovery of magnetization after resonant

excitation. The value depends on the static magnetic field strength and indirectly reflects

molecular dynamics within tissue microstructure. From an experimental standpoint, the

value is important for determining the optimal repetition time (TR) between spectral

acquisitions so that signal-to-noise ratio (SNR) is maximized (eg. according to the efficiency

63

metric √ ). Together with knowledge of the relaxation time, the value also

enables correction of spectral components to estimate absolute concentrations.

The values for the major spectral components of brain tumors have been well

studied at 3 T122, the preferable field strength for clinical MRS in terms of SNR and spectral

resolution. Lactate is the notable exception, however. Despite the importance of lactate as a

biomarker of tumor aggression, measurements of lactate are challenging because of three

inter-related factors. First, lactate is not typically detectable by MRS in normal brain,

necessitating that dedicated efforts must be made to measure values in patients prior to or

during their cancer treatment. Second, the most common methods for measurements are

time-consuming (see below). Third, the measurements of the lactate methyl [CH3] doublet

(coupled to a methine [CH] proton) are confounded by spectral overlap from lipids. To our

knowledge, the only pertinent human MRS data were acquired at 1.5 T without accounting for

the overlap123.

The present work was conceived to address these challenges and fill the gap in the

existing literature through dedicated study of brain cancer patients. Regarding the method for

measurement, the two most common choices are progressive saturation recovery124 and

inversion recovery (IR)125. When naively applied, both are inherently slow and suboptimal for

measuring low concentrations of in vivo brain metabolites in a time efficient manner in

patients, because they conventionally require use of a TR value that is multiples of the T1 value

of interest. More rapid methods such as the Look-Locker approach126 are difficult to combine

with the required spatial localization and spectral editing schemes. Alternatively, the modified

fast inversion-recovery (MFIR) method enables T1 measurements without full longitudinal

recovery127. This approach has been modified, characterized and validated appropriately for the

present spectroscopic application, allowing flexible choice of TR and a simple fitting approach

to estimate T1 values. In addition, the lactate and lipid signals are separated using the

radiofrequency (RF) band selective inversion with gradient dephasing (BASING)128 lactate-

editing sequence. The BASING sequence also provides lactate refocusing129, offsetting the

“anomalous” J-modulation24 and substantial signal reductions at odd multiples of 1/J that are

commonly observed using point resolved spectroscopy (PRESS)4. The anomalous J-modulation

64

of lactate is the result of the excited magnetization of the quartet and the doublet being slightly

shifted in space relative to each other. This shift results in voxel boundaries where both coupled

groups are not affected by both the refocusing pulses, resulting in a change in the phase of the

measured doublet signal. This results in some signal cancellation at the voxel boundaries,

thereby reducing the overall amplitude of the doublet signal. This approach also elevates the

available signal-to-noise ratio (SNR) by enabling robust lactate data collection at an echo time

of 144 ms (the most common clinical choice of long echo time) rather than 288 ms.

In summary, the present work addresses two aims. The first aim is to develop and

validate a novel MRS pulse sequence for measuring T1 values of metabolites in a time efficient

manner, suitable for use in patient populations and when J-coupled metabolites are present in

low concentration. The second aim is to use the developed pulse sequence to report the T1

value of lactate at 3 T in patients with brain cancer. In particular we measure the of the

lactate doublet averaged over all microenvironments, as it is typically what is of interest in

clinical MRS. These values, taken together with values from the literature as appropriate, are

then used to provide a quantitative estimate of the brain lactate concentration in vivo.

3.2 Theory

A diagram of the prototype pulse sequence for spectroscopic T1 measurement is shown in

Figure 3.1. The main elements consist of an inversion pulse followed some time later (as

selected by the inversion time, ) by point resolved spectroscopy4 (PRESS) localization with

spectroscopic readout. However, rather than performing acquisitions for multiple values in

sequence, as in IR experiments, paired acquisitions are performed at each value. One

acquisition includes the inversion pulse, producing the signal ( ). The other acquisition is

performed without the inversion pulse, producing the signal ( ). When the paired signals are

subtracted to yield a difference signal, it can be shown by solving the Bloch equations that

if TR is chosen to track with (i.e., , where C is a constant) then can be

modeled by a simple two-parameter, monoexponential decay function involving the T1

relaxation time constant. The optimal choice of TI and TR values (given practical constraints,

such as the total time allowed for MRS measurement) can then be determined using

65

computational methods to estimate the T1 value and minimize the uncertainty of the estimate.

This overall approach allows measurements with comparatively small TR values without the

need for a third parameter in the monoexponential model. The approach also provides time

efficiency by removing the need for all measurements to be made with large TR values for full

longitudinal recovery of magnetization, as adopted in conventional IR, and removes the need

for a fourth TI point as in MFIR130. A similar pulse sequence approach for T1 measurement has

been used recently, although without thoroughly validating optimal TI and TR selection as well

as time efficiency in relation to other measurement methods131. These issues are addressed as

part of the experimental methods outlined below.

3.3 Methods

Spectroscopic data were collected using a GE 750MR 3.0T MRI system (General Electric

Healthcare, Waukesha WI) with a standard 8-channel head coil receiver. Considering the

prototype pulse sequence (Figure 3.1) in more detail, the standard PRESS sequence was

modified to include four additional elements: 1) a frequency-selective (not spatially selective)

hyperbolic secant inversion pulse was added prior to the chemical shift selective saturation19

water suppression; 2) BASING RF pulses132 were added after the first and second refocusing

pulses, using linear-phase Shinnar-Le Roux75 (SLR) design with minimal transition width, to

reduce unwanted coherences and reduce co-editing of metabolites other than lactate; 3)

custom SLR 180° refocusing pulses were used to increase SNR and further reduce unwanted

coherences; and 4) the crusher gradient scheme was modified to include bipolar gradients

which selected the proper echo pathway for BASING. BASING is a two-cycle technique which

refocuses the lactate doublet (by inverting the lactate quartet) on the first cycle, and leaves the

lactate doublet unaffected on the second cycle. Subtraction of these two cycles results in the

addition of the lactate doublet, whereas the overlapping lipid signals are theoretically

unaffected by the cycling scheme and thus cancelled. Table 3.1 provides the details of all the RF

pulses within the sequence. The pulse sequence uses a four cycle scheme: two for the

separation of coupled and uncoupled resonances via BASING, and two for the interleaving of

the inversion pulse. For the first and second cycle, the inversion pulse is on, and for the third

and fourth cycle the inversion pulse amplitude is set to zero. For the first and third cycle, the

66

Figure 3.1: Spectroscopic pulse sequence for measuring T1 relaxation. is the inversion pulse

and TI is inversion time. A subsequent PRESS module is characterized by the time between

the isoflip time of the excitation pulse ( ) and the middle of the first refocusing pulse ( );

the time between the middle of and the second refocusing pulse ( ); and , the

time between the middle of and the start of the acquisition time. is equal to

the echo time (TE), 144 ms. The time between the first BASING pulse ( ) and the second

BASING pulse ( ) is TE/2 (72 ms) for this editing scheme.

BASING pulses ( and in Figure 3.1) are centred at water and invert the lactate quartet

at 4.1 ppm. For the second and fourth cycles, the BASING pulses are shifted downfield by 198

Hz to not invert the metabolites. Thus the uncoupled singlet signal, , is obtained from

( ) (3.1)

where the subscript on denotes the cycle number of the measured signal. The coupled

doublet signal, , is obtained from

67

( ) (3.2)

This four cycle scheme is then repeated as necessary for signal averaging. For the work

presented here, 128 excitations were performed for each value. Thus, the four cycle scheme

was averaged over 32 trials in each case.

The signal processing was performed using specially-written scripts in MATLAB (the

Mathworks, Inc., Natick, MA). Prior to reconstruction, the first point in the raw FID of the

unsuppressed water acquisition was used to estimate the coil sensitivity, which was then used

to rephase and scale the signals from each of the individual coils. By scaling and phasing prior to

summation over the eight coils, SNR is substantially improved compared to direct summation.

The net signals were then combined over the four-cycle scheme according to Equation 3.1 for

singlets and Equation 3.2 for lactate, averaged over all repetitions as appropriate, and input to

the freeware known as Totally Automatic Robust quantitation in NMR (TARQUIN94). Algorithms

within TARQUIN enabled fitting of the data to the four metabolites of interest: NAA, lactate,

creatine and choline. Because the BASING pulses have some effect on some downfield smaller

peaks, a more physically realistic basis set was used by taking the default basis set in TARQUIN

and removing the smaller resonances.

The concentration values obtained from TARQUIN were then used to estimate values. For

computational simplicity, the monoexponential equation for was linearized by taking the

natural logarithm and weighting the noise contribution at potential TI values appropriately.

This enabled use of linear least squares fitting to estimate T1, and optimization of TI and TR

values using a closed-form expression to minimize the uncertainty of the estimate. White

Gaussian noise was assumed and standard error propagation was used to derive the expression

for estimating the standard deviation of , . To estimate reliably while accounting for

imperfect model fitting or spurious artefacts in the experimental data which were not well

represented by the standard deviation of the spectral components, the calculation was

performed using both the Cramer-Rao lower bounds from TARQUIN, and from the standard

deviation of the residuals of the least squares fit. The larger of the two estimates was reported.

68

The unsuppressed water acquisition was then used to provide an absolute reference, setting

the concentration of water at 42.3 M, as is common when attempting to quantify metabolite

Table 3.1: Summary of radiofrequency (RF) pulses in the prototype pulse sequence (Figure 3.1).

Pulses and are centred on water for the first and second cycles of the four-cycle

scheme (and invert the lactate quartet) and for the third and fourth cycles they are shifted

downfield from water to leave the metabolites unaffected. Pulse inverts all the metabolites

for cycles 1 and 2 but is set to zero for cycles 3 and 4. Shinnar Le-Roux75 (SLR) pulses are used

for all but the inversion pulse, and BASING128 pulses are the minimum-phase SLR pulses used

for the lactate-editing sequence. B1 is the amplitude of the applied RF pulses.

Name Pulse type Pulse duration (ms)

Bandwidth (Hz)

Offset frequency (Hz)

Max B1 ( )

Custom adiabatic hyperbolic secant

10.0 1200 -256 16.01/0

Stock excitation SLR

3.6 2367 -256 14.22

Custom spin-echo SLR

9.5 842 -256 17.66

Custom minimum-phase BASING SLR

30.0 230 0/198 5.74

Custom spin-echo SLR

9.5 842 -256 17.66

Custom minimum-phase BASING SLR

30.0 230 0/198 5.74

concentrations from MRS data64. All concentrations were obtained using water as an internal

reference, as implemented in TARQUIN, similar to the procedure by Madan et al.35 The water

unsuppressed signal had its concentration set to be 42.3 M, with a water attenuation factor of

exp(-TE/T2water), where the T2 of water was 56 ms for healthy white matter or 156 ms for glioma

tissue35. For each metabolite the straight line intercept of the concentration vs TI plot was used

to correct for inversion effects. This value was then corrected for non-equilibrium steady-state

69

T1 effects and T2 relaxation using T2 values obtained from the literature for lactate35 and the

other metabolites122. Statistical comparison between the T1 of lactate and the other three

metabolites was subsequently performed using a Wilcoxon signed-rank test, with the threshold

for statistical significance set according to a Type 1 error of .

The additional pulse sequence parameters for this initial work included an echo time

(TE) of 144 ms (the TE value providing the maximum SNR achievable with this editing

scheme128) and a voxel size of 20 mm 20 mm 20 mm. Water suppression was

implemented using the standard CHESS19 method immediately preceding the excitation pulse

for all inversion times.

The uncertainty on T1 derived from the least squares fit was numerically minimized to

yield the optimal sets of TIs and TRs. The optimization was subject to three physical constraints:

(the minimum allowable inversion time to perform CHESS prior to the excitation

pulse); TR exceeds the time from pulse sequence onset to the end of readout;

(necessary for the monoexponential model to hold); and a fixed total experiment time (chosen

for in vivo work to be 30 minutes, ignoring calibration time). The number of individual

measurements (i.e., TI points within the 30 minute measurement time) was allowed to vary

from 3 to 8. Numerical minimization of the expected uncertainty on T1 was achieved using the

iterative sequential quadratic programming algorithm in MATLAB that required a priori

knowledge of the value of interest. The only inputs required to optimize the choice of TI and

TR values were the estimated T1 and the total experiment time. The algorithm commenced with

a starting value of , the reported of rat glioma measured at 4.7 T60, to

approximate the expected human analogue.

Briefly summarizing the subsequent experiments, tests were first undertaken in

phantoms to confirm that the prototype pulse sequence produced results in agreement with

a standard interleaved inversion recovery sequence with equilibrium achieved in-between

successive excitations. Next, validation experiments were undertaken with two healthy

volunteers (23 year old male and 36 year old female for volunteer 1 and 2, respectively) to

ensure that the sequence provided physically realistic measurements of relaxation in vivo.

70

Finally, the sequence was used to estimate lactate in a group of patients with brain cancer.

All volunteers participated with free and informed consent and with the approval of the

research ethics board at Sunnybrook Health Sciences Centre.

The values of lactate, NAA, creatine and choline were initially estimated from the

“Braino” phantom (GE Healthcare). As a proof of principle the measurement was done using

four linearly spaced values of 160, 260, 360, 460 ms using both the prototype pulse

sequence, and for comparison an inversion recovery (IR) PRESS sequence with an interleaved

adiabatic hyperbolic secant inversion pulse76,77 added prior to water suppression with a TR of 4

seconds. The TI, TR as well as the total experiment time for all experiments are shown in Table

3.2. For these initial measurements, identical values were used in both sequences to control

for possible systematic variation in the precision on associated with the sampling of

longitudinal relaxation. The TR values for the prototype pulse sequence were 1.5, 1.6, 1.7 and

1.8 s, respectively, with a total experiment time of 14 minutes. For the standard IR PRESS

sequence, an appropriate TR of 4 s was chosen to allow close to full recovery between each

successive inversion for the metabolites of interest, with a total experiment time of 34 minutes.

For practical application of the prototype pulse sequence, the optimal values were

determined for a given total acquisition time according to the optimization procedure

described above. Notable exceptions were that a) the optimization algorithm was run with an

initial value of T1 = 720 ms (the variance-weighted mean of the previous two measurements of

the T1 of lactate in this phantom) and b) the duration of the experiment constrained to 14

minutes, as for application of the prototype pulse sequence above. The TIs for the optimized

measurements in the “Braino” phantom were 160, 942 and 942 ms, and the associated TR

values were 1680, 2460 and 2460 ms for the three points, respectively. A similar numerical

optimization algorithm was written for the standard IR sequence which constrained the time

between the 2nd refocusing pulse and the start of the next TR interval to equal 5T1. For this

sequence the number of TIs and the number of averages were allowed to vary while keeping

the total experiment time equal to the experiment time of the prototype sequence. The

optimal TIs were determined as 160, 897 and 897 ms and the associated TR values were 3909,

4646 and 4646 ms, respectively, with 64 averages performed at each TI.

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For in vivo work, the prototype pulse sequence was used to measure T1 with the

optimization algorithm prescribing TI values of 160, 2011 and 2011 ms with TR values of 3454,

5304 and 5304 ms, respectively. (The larger prescribed in vivo TIs and TRs are due to the larger

in vivo T1 values that are expected in comparison to those of the Gadolinium-doped “Braino”

phantom).

Table 3.2: The TI, TR and total scan time values for all experiments.

TI1

(ms) TI2

(ms) TI3

(ms) TI4

(ms) TR1

(ms) TR2

(ms) TR3

(ms) TR4

(ms) Total scan time (minutes)

“Braino” IR

160 260 360 460 4000 4000 4000 4000 34.13

“Braino” prototype

160 260 360 460 1500 1600 1700 1800 14.08

“Braino” IR optimized

160 897 897 n/a 3909 4646 4646 n/a 14.08

“Braino” prototype optimized

160 942 942 n/a 1680 2460 2460 n/a 14.08

In vivo prototype optimized

160 2011 2011 n/a 3454 5304 5304 n/a 30.00

When performing MRS of patients, care was taken to maximize the amount of tumor

tissue within the voxel by comparing the voxel placement with -weighted anatomical images

acquired after administration of Gd-DTPA contrast agent from previous exams, which were

available in 3 of the 6 patients. Care was also taken to avoid placing the voxel too close to the

skull or ventricles. A representative anatomical image with overlaid voxel placement is shown in

Figure 3.2.

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3.4 Results

Figure 3.3 shows the results of the four relaxation measurements (prototype sequence and

IR-PRESS sequence with the same linearly-spaced values, the prototype sequence with

optimized values, and the optimized IR sequence) for the four main metabolites in the

“Braino” phantom. The measurements are in excellent agreement in all cases, as evident by

observing the similar slopes of the plots (proportional to

) for each metabolite, and the

Figure 3.2: T1 – weighted anatomical image with voxel placement (white square) overlaid for

patient 5. The singlet and doublet spectra at both long and short TI are displayed in Figure 3.4

for this patient.

differences are comparable to the measurement error. For display purposes, the amplitudes

have been arbitrarily scaled to distinguish each line easily. However, the steady-state values for

the prototype sequence versus the standard IR sequence are lower by 9%, 3%, 18% and 15%,

for creatine, lactate, NAA and choline, respectively. The decrease observed for lactate is

73

substantially less than the other three metabolites despite its relatively large value due to

the refocusing effect of BASING129, as explained further in the discussion. The metabolite

concentrations within the phantom were also estimated as described in the Methods section,

using the values of Figure 3.3 as well as previously measured T2 values. Values of

mM, mM, mM, and mM were estimated for the concentrations

of creatine, NAA, choline and lactate, respectively. The singlets are within approximately 10 %

of the known concentrations133 of 10 mM, 12.5 mM and 3 mM for creatine, NAA and choline,

respectively, whereas the lactate doublet is within approximately 25 % of the known

concentration of 5.0 mM. The lower estimate for lactate is likely due to residual anomalous J-

modulation24.

Figure 3.3 also shows that the estimation error is largest for lactate, due to its lower

concentration (and thus SNR) within the “Braino” phantom. The error on the estimated T1 of

lactate is reduced by a factor of 0.41 for the optimized prototype pulse sequence, compared to

the error obtained when using the arbitrary linearly spaced values. The reduction factor

predicted by the optimization algorithm is 0.47, in good agreement with this result. The error

on the estimated T1 of lactate is reduced by a factor of 0.74 for the optimized prototype

sequence compared to that obtained with the optimized IR sequence. The numerical

optimization predicts a reduction factor of 0.82, again in good agreement. Overall, these results

indicate a) that substantially improved precision of the estimate can be obtained if the

value is known reasonably well a priori; and b) that by removing the constraint of large TR

values, an improvement in precision can be gained for a constant experiment time.

In addition, the technique developed was applied to probe the white matter of two

healthy volunteers, producing the estimated values for choline, NAA and creatine shown in

Table 3.3. Adequate agreement is obtained with previous estimates122,131,134, with the present

estimate slightly elevated for creatine. Using these values, the mean metabolite concentrations

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Figure 3.3: Inversion recovery results for “Braino” phantom for the four major metabolites

observed at TE = 144 ms (creatine, lactate, NAA, choline). The top line for each metabolite

corresponds to the prototype pulse sequence applied with optimized choice of TI and TR. The

second from the top line is the optimized interleaved IR technique. The second from the

bottom line is the prototype pulse sequence with linearly spaced inversion times, and the

bottom line is the standard interleaved IR technique applied with TR = 4000 ms. Amplitude

values were scaled arbitrarily for display purposes. The acquisition time for both applications of

the prototype pulse sequence and the optimized IR sequence was the same (approximately 14

minutes), and the acquisition for the linearly spaced IR sequence was approximately 34

minutes. The slope of the line is proportional to and the parameter estimates are also

shown including error estimates. Excellent agreement is observed between measurements for

all four metabolites. Substantially reduced error in estimating was obtained for both lactate

and NAA when using the optimized TI and TRs for both the prototype and the standard IR

sequence, as predicted by numerical simulation.

75

for the two healthy volunteers were estimated as mM, mM and

mM for choline, NAA and creatine, respectively. The concentration estimates are within the

range previously reported in the literature of 1-5 mM for choline, 10-25 mM for NAA and 6-14

mM for creatine135 and in excellent agreement with those reported using a very similar

absolute quantitation scheme35. No lactate was detected within the doublet spectra from the

healthy volunteers, as expected.

Table 3.3: T1 values measured within the white matter in two healthy volunteers as well as the

mean values obtained from healthy volunteers within the literature at 3 T.

Subject Choline T1 (ms) NAA T1 (ms) Creatine T1 (ms)

1

2

Chen, et al.131

Li, et al.122

Träber, et al.134 (inversion recovery)

Träber, et al.134 (saturation recovery)

Figure 3.4 shows the spectra obtained from a grade 3 glioma according to the voxel

prescribed in Figure 3.2, for the optimized values of 160, 2011 and 2011 ms. There is good

singlet suppression in the doublet spectra, consistent with previous work132 (as shown by the

significantly reduced amplitude of NAA, Cr and Cho), indicating negligible contamination from

lipids in the vicinity of lactate. Table 3.4 lists the estimated T1 values for lactate, choline, NAA

and creatine across the group of patients. The T1 estimates for choline and NAA agree well with

those obtained previously from glioma patients122, although the value presented here for

creatine is slightly larger. Potential reasons for this discrepancy are given in the discussion.

There is a significant difference between the T1 of lactate and choline (P = 0.004) as well as

lactate and NAA (P = 0.009). No significant difference is observed between the T1 of lactate and

76

creatine (P = 0.537). Given the estimated T1 of lactate, calculations indicate that √ is

maximized for a standard PRESS sequence with TE = 144 ms or TE = 288 ms when TR = 2830 ms.

Figure 3.4: Spectra obtained from patient 5 (from the voxel prescribed as shown in Figure 3.2).

The top row shows singlet spectra (choline, creatine, NAA and lipids) whereas the bottom row

shows doublet spectra (primarily lactate). Spectra in the left column were acquired with TI =

160 ms and those in the right column were acquired with TI = 2011 ms. Only two TI values are

displayed, since the second and third acquisitions of the optimized prototype pulse sequence

used the same TI value and the respective spectra differed only by noise. Note that there is

some non-lactate signal in the doublet spectrum in the 2-3 ppm range, likely due to the co-

editing of NAA and glutamate.

Table 3.5 displays the absolute concentrations for the four measured metabolites for all

six glioma patients. Good agreement is observed between the present results and those

previously estimate35,136 by a very similar absolute quantification procedure. Most importantly,

the lactate concentration estimate of ( ) mM is within experimental variation of the

previously reported values of ( ) mM35 and ( ) mM136.

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Table 3.4: Estimated T1 values from six patients with high grade glioma. The measured spectra

from patient 1 indicated very large lipid, lactate and water peaks with minimal other

metabolites indicating that the voxel was placed primarily within a necrotic core; T1 could only

be measured for lactate in this patient. All patients were diagnosed with glioblastoma (grade 4),

except patient 5 who had a grade 3 glioma. The third last row is the mean standard deviation

across all patients. The second last row is a previous measurement of the T1 values obtained

from a population of glioma patients at 3 T. The last row is a previous measurement of the T1

values obtained from a population of brain tumor patients at 1.5 T.

Patient number Lactate T1 (ms) Choline T1 (ms) NAA T1 (ms) Creatine T1 (ms)

1* n/a n/a n/a

2

3

4

5

6

Mean

Li et al.122 n/a

Sijens et al.123 (1.5 T)

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Table 3.5: Estimated absolute concentration of metabolites from the same patients with high

grade glioma as reported in Table 3.3. Spectra from patient 1 indicated very large lipid, lactate

and water peaks with minimal other metabolites indicating that the voxel was placed primarily

within a necrotic core. All patients were diagnosed with glioblastoma (grade 4), except patient

5 who had a grade 3 glioma. The second last row is the mean standard deviation across all

patients. The bottom row is a previous measurement of the absolute concentration values

obtained from a population of high grade glioma patients at 3 T.

Patient number [Lac] (mM) [Choline] (mM) [NAA] (mM) [Creatine] (mM)

1* n/a n/a n/a

2

3

4

5

6

Mean

Madan, et al.35

3.5 Discussion

In the present work, a novel pulse sequence was developed for measuring of low-

concentration J-coupled species in a time-efficient manner. This sequence was then applied to a

population of high grade glioma patients to measure the of the methyl group of lactate in

vivo.

A number of issues are worth discussion in relation to the pulse sequence that was

developed. First, the numerical procedure for minimizing in a specific experiment duration

(30 minutes in humans) prescribed that data should be acquired at a single low TI value, and

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two identical high TI values. This prescription is not intuitive, however, it is understandable by

thinking about simple linear least squares procedures for estimating the slope of a line. In such

cases, the error on the slope has a dominator term equal to ∑ ( ), where x is the

independent variable, is the mean, and the subscript i refers to each instance of the

independent variable. The error on the slope is thus minimized if the values are spread

widely apart in relation to . This accounts for the wide separation in TI values that was

prescribed. In addition, the latter TI value has low SNR (as almost full recovery has occured)

and so repeated measurements are prescribed to obtain a more precise estimate of the signal

at this time point.

A related issue concerns linearization of the monoexponential equation for which

was performed to simplify computational aspects of optimizing TI and TR values for the

prototype pulse sequence. For the TI values with low SNR, no correction was made for how the

noise distribution was skewed by taking the natural logarithm. To check for potential

systematic error due to this approach, T1 estimates were re-calculated using nonlinear least

squares fitting of the monoexponential function. The nonlinear fit results deviated by <1 % from

the values listed in Table 3.3 and Table 3.4. Thus, linearization was useful in the present case,

but it is not essential to the success of the method and may not be advisable for data acquired

at lower SNR levels.

The prototype pulse sequence was also found to be robust to experimental

imperfections. Numerical simulation of the Bloch equations revealed that variation in

the prescribed flip angle of the excitation and refocusing pulses perturbed a T1 value of 2000 ms

by only ms, well within the biological variation of the experimental results. No obvious

signs of unwanted coherences were evident in the acquired spectra, possibly because

decreased spoiling power is required for the coarse voxel size used here26. More gradient

spoiling could be added to the sequence in the future, if required. Slightly increased noise was

consistently observed for the 2-3 ppm range in both the “Braino” phantom and volunteers,

however (eg. See Figure 3.4). This noise did not affect quantification of lactate, and likely

results from co-editing of J-coupled resonances of NAA and glutamate which are known to lie

within the pass band of the BASING pulse27. The effect of voxel shift due to chemical shift mis-

80

registration is also unlikely to be important because it can be reasonably assumed that all

lactate originates from the tumor, which was typically larger than the prescribed MRS voxel.

Lastly, water suppression could be achieved solely through BASING9 by modifying the frequency

scheme of the BASING pulses. This was not implemented here, as CHESS has been shown to be

robust across a range of static magnetic field strengths, whereas simultaneous spectral editing

and water suppression with BASING is challenging at 1.5 T due to spectral overlap effects with

the pulses used here. However, sole use of BASING could reduce the minimum allowable TI to

7.6 ms (down from 160 ms), which could help to improve measurement precision. Overall, the

quality of the spectra was judged to be similar to that obtained in previous work using the

identical BASING scheme132.

The optimal choice of TI values has previously been investigated for MFIR130. Similar

results were obtained in comparison to the present work, with optimal results obtained when

one low TI value and one high TI value were selected. The effect of low SNR motivating a

repeated measurement at the high TI value was not considered. In contrast, Ogg and

Kingsley130 have suggested taking measurements with two additional intermediate TI values.

Differences between the TI prescriptions are likely due to the differences in underlying

mathematical model. The present work uses a two-parameter mono-exponential model which

is appropriate under MRS conditions, whereas Ogg and Kingsley adopted a three-parameter

model more appropriate for proton MRI with higher SNR130.

Although a priori knowledge of the T1 is required for numerical minimization of the

uncertainty on T1, this is not a major limitation and is not a requirement for the

monoexponential model of to hold. In practice, the initial input of T1 for optimization did

not have a strong impact on the prescription of optimized TI and TRs. If the actual value of

varies from the input initial value of = 1725 ms by plus or minus 500 ms the result is an

increase in the predicted uncertainty by a factor of 1.11 and 1.02, respectively. In any case, this

is not a limiting issue. Some implicit knowledge of is always assumed when choosing TI

values to sample recovery curves appropriately, as part of obtaining high quality results in

inversion or saturation recovery experiments.

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The MRS measurement precision in patients likely was also reduced compared to the

data quality that is achievable in healthy humans119. This is expected because of the lower

signal from tumor tissue and the difficulty in maximizing the amount of tumor tissue within the

voxel. These effects are general to the use of single voxel MRS and not specific to the pulse

sequence. However, TI values were chosen to optimize T1 measurements for lactate, which

recovers in the longitudinal direction more slowly than some of the other metabolites, slightly

compromising their measurement. Other MRS studies134 measuring the longitudinal relaxation

within tumors have achieved coefficients of variation (ratio of the standard deviation to the

mean) for choline, creatine and NAA of approximately 20%, similar to the values observed here.

The T1 estimate for creatine was slightly larger than previously measured122,123, which could be

due to differences in the patient populations studied, voxel positioning or other sources of

statistical variation. The discrepancy likely did not result from an imperfection in the model

used here. Such a systematic bias likely would impact NAA and choline results as well, whereas

these showed excellent agreement with the literature.

With appropriate modification, the technique presented here could also be used in a

variety of other measurement applications, such as to estimate the T1 value of 2-

Hydroxyglutarate64. Furthermore, the technique is not constrained to single voxel applications.

It could be incorporated in a typical MR spectroscopic imaging technique, or a multivoxel

technique that avoids or minimizes k-space encoding, such as Hadamard encoding137 or

constrained source space MRS138,139. This could be advantageous for measuring the value of

metabolites in pathologies with large heterogeneous regions, or to expedite control

measurements from regions of healthy tissue. The lactate-editing scheme chosen here is not

the only available technique for disentangling lactate and overlapping lipid signals. The

inversion sequence here could use the LASER (localization by adiabatic selective refocusing)

method instead, which substantially reduces the chemical shift mis-registration, and the

separation of lactate and lipid signals can be done using a multiple-quantum filter140. Other

alternatives are MEGA-SPECIAL141, which adopts a similar editing technique referred to as

MEGA for spectral separation and SPECIAL for spatial localization, or similarly MEGA-sLASER142.

These options could potentially improve SNR and, since the multiple-quantum filter is a single

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shot technique, reduce sensitivity to motion at the cost of signals from the other metabolites.

For BASING to provide perfect lipid suppression, the spectra in the two-cycle subtraction

scheme must have identical phase and amplitude and thus there is the potential for motion to

introduce errors. However, the present results suggest that such errors are small in practical

circumstances.

The lactate T1 values reported for brain cancer patients in the present work agree well

with those for rat gliomas at 4.7 T60 but are substantially higher than those measured

previously at 1.5 T in a separate population by another research group who did not include

spectral editing123. It is possible to add the signals from the lipid and lactate resonances in the

present work, representing the scenario whereby measurements are made without use of

BASING. This provides a estimate of ( ) ms that is within experimental

uncertainty of the previous estimate of ( ) ms at 1.5 T123, strongly suggesting

that lipid contamination produces a substantial erroneous reduction in lactate . There is a

significant difference between the of the lipid plus lactate compared to the lactate alone

(P = 0.004). This discrepancy could also be partially due to the differences in field strengths

used for measuring (3 T here vs 1.5 T previously123), as in many biological compounds the

value exhibits a field strength dependency. It should be noted that there exists a correlation

between the T1 relaxation times measured from Patients 2-6 between lactate and the other

metabolites, as evident by the R2 values of 0.45, 0.67 and 0.26 for NAA, Cr and Cho,

respectively.

It should also be mentioned that the estimates provided here are probably slightly

different from the "true" values that would be obtained for each resonance in the absence of

magnetization transfer and chemical exchange effects. Such effects likely occur at some level in

the present experiments, for example due to use of CHESS pulses. This water suppression

scheme is very common, and the vast majority of in vivo MRS work using this and other

schemes does not account for exchange effects. Investigating the extent of perturbation

due to magnetization transfer or chemical exchange is an interesting topic for future research

which would likely benefit from the use of a time-efficient procedure for precisely measuring

longitudinal relaxation, such as developed here. From a practical standpoint, the present work

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makes the appropriate estimates that are required to correct for partial longitudinal

recovery, as well as for optimizing √ in MRS experiments.

The absolute concentration values that were estimated in the present work are also in

good agreement with previous reports35,136. Overall, the general trend was confirmed of

increased lactate and choline, decreased NAA and roughly equal creatine concentration for

glioma tissue as compared to healthy tissue35. These estimates are based on the assumption

that the water resonance has a constant concentration of 42.3 M35,64, however, whereas the

actual concentration within glioma tissue could vary from this value, as well as from patient to

patient. This limitation can be removed by including an external reference sample in the

measurement protocol and data analysis. For now, clinical MRS will continue to rely on

interpreting MRS data using ratios of neurometabolite signals ratios with respect to creatine

(which has been shown to vary substantially within a patient population35). Irrespective of this,

the use of reference samples, literature values for and for each spectral component

(including the revised data for lactate reported here), enable absolute concentrations to be

estimated for use in basic and clinical MRS research. In the future, such work may facilitate the

use of lactate as a biomarker for improved cancer treatment and survival.

3.6 Conclusions

A novel MRS technique was developed and validated at 3 T for time-efficient measurements

of the methyl group of lactate without contamination from lipids. The resulting T1 value of

(2000 280) ms was obtained for a group of six glioma patients. After correcting for T1 (and T2

from literature values) the absolute lactate concentration was estimated as ( ) mM.

Lactate T1 exhibits similar variations as other major metabolites observable by MRS in high

grade gliomas. The T1 estimate provided here will be useful for future MRS studies that wish to

optimize pulse sequence parameters, or to report relaxation-corrected estimates of lactate

concentration as an objective tumor biomarker.

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Chapter 4

Diffusion-Weighted J-Resolved Spectroscopy

A paper published in Magnetic Resonance in Medicine [Epub ahead of print] by Karl Landheer,

Rolf Schulte, Ben Geraghty, Christopher Hanstock, Christian Beaulieu, Albert P. Chen, Charles H.

Cunningham, and Simon J. Graham

4.1 Introduction

Proton magnetic resonance spectroscopy (MRS) is a powerful non-invasive technique used to

measure biomarker activity within the brain and body. The diffusion characteristics of the

spectral components can also be investigated by appending diffusion-sensitizing gradients to

standard protocols such as Point Resolved Spectroscopy4 (PRESS) and Stimulated Echo

Acquisition Mode113 (STEAM), as implemented by Posse et al.101 These techniques, referred to

as diffusion-weighted magnetic resonance spectroscopy (DW-MRS), provide novel information

and allow metabolites and their microstructural environment to be probed noninvasively within

the intracellular and extracellular space in vivo. The DW-MRS data features depend on factors

such as active transport, cytosol viscosity and compartmentalization. With the exception of

glucose and lactate, the metabolites that can be probed in this manner are predominantly

intracellular143.

In principle, each individual metabolite offers specific information about the

microstructural compartments where it resides. Previous DW-MRS results show that

metabolites diffuse freely along cell fibers, suggesting that the metabolites are not confined

inside cell bodies103. The pathophysiological changes in DW-MRS signals have been measured

for a range of disorders such as cancer, ischemia and excitotoxicity of the brain143, in addition to

glial reactivity in response to inflammation in systemic lupus erythematosus106. Functional MR

techniques have also been combined with DW-MRS to observe increased apparent diffusion of

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metabolites during visual stimulation,107 providing a unique tool for investigating physiological

aspects of brain activity.

To date, DW-MRS of the human brain has primarily focused on the N-aceytlaspartate

(NAA), creatine (Cr) and choline (Cho) resonances due to their relatively high signal-to-noise

ratio (SNR) and strong non-overlapping singlets. Other metabolites are of substantial interest,

however. For example, glutamate (Glu) and gamma-aminobutyric acid (GABA) have important

roles as the main excitatory and inhibitory neurotransmitters, respectively46. Glutamate and

GABA typically reside in the synaptic vesicles29,46 within neurons, but during neurotransmission

they are released into the synaptic cleft where they bind to postsynaptic receptors. To the

authors’ knowledge, however, only three proton DW-MRS studies have investigated

metabolites in the human brain beyond NAA, Cr and Cho: glutamate, glutamine (Gln) and N-

acetyl aspartyl glutamate (NAAG) in healthy volunteers at 7 T108, lactate in edema and tumors

at 3 T144 and myo-inositol (mI) in healthy volunteers at 3 T145. The major difficulties in

measuring such other metabolites are: 1) low SNR, necessitating long scan time; and 2) spectral

overlap with larger resonances. These problems are particularly troublesome at field strengths

of 3 T and below. Alternatively, using higher field strength systems for small animal MRI, the

diffusion characteristics of up to 10 metabolites have been investigated in ischemic rat brains at

4.7 T146 and 12 metabolites have been investigated similarly in healthy rat brain at 9.4 T147.

Several MRS techniques have been developed for accurately measuring smaller

resonances in the absence of diffusion weighting. Spectral editing J-difference techniques have

been developed on clinical-grade MRI systems for detection of Glu and Gln8, GABA148, lactate129

and 2-Hydroxyglutarate64, among others. These techniques have good efficacy but usually

require the phase of two subsequent acquisitions to be consistent. It is well known that the

phase varies greatly from one excitation to the next in DW-MRS due to involuntary subject

motion, however, necessitating re-phasing of the spectra prior to summation101. Re-phasing is

a challenge under conditions of low SNR and this may negatively impact the ability to edit and

quantify the smaller resonances of interest. A small deviation from the 180° phase difference

requirement in spectral editing can lead to substantial error. In addition, spectral editing

techniques often improve detection of specific resonances while disrupting other portions of

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the spectrum. Two-cycle spectral editing is probably not ideal for measuring the diffusion

properties of small resonances, therefore, in many cases.

An alternative spectral editing scheme based on double-quantum coherence-transfer

has been used to measure the diffusion of lactate in the presence of contaminating lipids149–151,

but discards information about other metabolites. Another approach uses biexponential fitting

to estimate the apparent diffusion coefficient (ADC) of lactate within a tumor in the presence of

lipids152. This method is well suited to obtaining lactate ADC values, but would be challenging to

apply to other metabolites such as glutamate due to spectral overlap, and the need for very

high SNR to discern each metabolite properly.

Small resonances can also be quantified at 3 T using localized two-dimensional (2D) J-

resolved spectroscopy (JPRESS)13,153, originally implemented in a half-echo acquisition mode. In

JPRESS, a second spectral dimension is added by sequentially increasing the echo time for each

spectroscopic data acquisition. As the initial phase of the signal collected from a J-coupled

metabolite varies depending on the echo time, collecting data at a series of different echo

times enables careful sampling of the dispersion in this second spectral dimension. Subsequent

2D Fourier transformation of the time domain data into the frequency domain enables

quantification of some of the smaller resonances which are overlapped in traditional one-

dimensional (1D) spectroscopy. The efficacy of the JPRESS sequence has been demonstrated on

clinical-grade 3 T MRI systems including acquisition of in vivo data, with a maximum echo

sampling scheme to improve sensitivity16. Using JPRESS and 2D fitting software referred to as

ProFit14,15, signals from 17 metabolites have been reliably measured in vivo at 3 T. Based on

these promising initial developments, a novel technique called diffusion-weighted JPRESS (DW-

JPRESS) is proposed here. The purpose of this study is to describe, characterize and validate use

of DW-JPRESS to measure the ADCs of metabolites beyond NAA, Cr and Cho at 3 T.

4.2 Methods

A prototype pulse sequence for DW-JPRESS was implemented using a MR750 3 T MRI system

(General Electric Healthcare, Waukesha WI) with a standard 8-channel head coil receiver. The

associated pulse sequence diagram is shown in (Figure 4.1), and includes three major

87

modifications from the basic JPRESS sequence. First, maximum-echo sampling was

implemented, whereby sampling consistently commenced 1 ms after the last gradient pulse,

instead of at the spin echo maximum as in typical MRS readouts. Maximum-echo sampling was

used because it has been shown to increase SNR and decrease overlap of resonance tails16. The

1 ms time delay following the last gradient pulse was included as a conservative measure to

allow for dissipation of any fast decaying eddy currents that were potentially present, prior to

commencing data collection. Second, diffusion-sensitizing gradients (DSGs) were added prior to

the first crusher and after the last crusher in the three orthogonal directions simultaneously.

Third, instead of automatically averaging the data from all identical excitations, all individual

spectral traces for identical excitations were saved for rephasing prior to averaging, as

necessary in DW-MRS101. The time between the start times of the DSGs, , was set to be 60.636

ms for the first TE value (74 ms), and incremented by = 1 ms for each TE step. The DSG width,

, was 8.396 ms with a rise time, , of 1 ms for all echo times (a slew rate of 50 T/s/m, ie. one

quarter of the maximum value available on the MRI system, 200 T/s/m). For in vivo

experiments, the amplitude of the DSG for the first TE value, , equaled 50 mT/m (the

maximum allowable gradient amplitude) and 5 mT/m for diffusion-weighted spectra acquired

with high and low b-values, respectively (see below for further details). The diffusion-weighting

parameters were chosen to provide a sufficiently large b-value for adequate diffusion weighting

while maintaining a short echo time. A non-zero b-value for the low b acquisition was used to

assist in suppressing unwanted echo pathways. Outer volume suppression154 and global water

suppression via CHESS19 were implemented using the standard pulse sequence components

available. Outer volume suppression was used both to reduce lipid contamination from the

scalp and for inner volume saturation25 to reduce anomalous J-modulation effects.

In each in vivo DW-JPRESS experiment, two data sets were collected with CHESS water

suppression on: one with the DSG amplitude set to the high b-value, and the other with DSG set

to the low b-value. In both cases, data were acquired with the number of excitations (NEX) set

at 8 for each TE step, for signal averaging purposes. Two additional calibration data sets were

collected with identical parameters except a NEX of 1 and water suppression off: one at high b-

value and one at low b-value. All 4 data sets were acquired with a minimum TE value of 74 ms,

88

Figure 4.1: DW-JPRESS pulse sequence for the a) initial echo time and b) intermediate kth echo time.

Shaded gradients are crushers. The large gradients played out in the three orthogonal directions are the

diffusion-sensitizing gradients (DSGs). Acquisition begins approximately 1 ms after the end of the second

DSG, for maximum echo-sampling. Note that the amplitude of the DSGs are reduced in b) compared to

a) according to Equation 4.3 to keep the b-value constant across all echo steps. The reduction in

amplitude is exaggerated for display purposes. The peak of the spin echo occurs at the echo time (TE)

and is equal to and the second refocusing pulse in b) is played out at a time later

than in a). Outer-volume suppression and global water suppression (CHESS) is executed prior to the 90°

excitation pulse. Gradient amplitudes are not drawn to scale (crushers are substantially smaller than the

DSGs for all TE values).

89

5000 Hz receiver bandwidth (corresponding to the bandwidth of the F2 dimension in the JPRESS

spectra), the aforementioned of 2 ms (corresponding to a bandwidth of 500 Hz in the F1

dimension) and 100 incremental TE steps. The water unsuppressed JPRESS spectra were used

for eddy current correction20 and estimation of coil sensitivity. Cardiac gating was also included

to reduce motion-related errors in vivo due to cerebrospinal fluid pulsatility, with the TR value

set to two R-R cycles (~2 sec) and a trigger delay of 300 ms101. For the phantom experiments

the TR was set to 1.5 s. For the phantom experiment performed on the “BRAINO” phantom,

two additional intermediate b-values were also collected (both the water suppressed and water

unsuppressed data). These data were subsequently used to compare ADCs estimated with four

b-values to those estimated with only two b-values, as part of validating the latter approach.

The signal processing of DW-JPRESS data was performed using specially-written scripts

in MATLAB (the Mathworks, Inc., Natick, MA) using a pipeline as shown in Figure 4.2. The data

from each coil were eddy-current corrected using the water-unsuppressed JPRESS spectrum20,

weighted by the coil sensitivity and then averaged over all eight coils. The coil-averaged signal

was then multiplied by a novel “streak correction” factor to maintain the water resonance at

constant amplitude across all NEX to correct any residual cardiac pulsatility artefacts not

eliminated by cardiac gating. The streak correction algorithm is similar to the algorithm

originally proposed by Posse et al.101 to reject individual data traces that exhibit excessive signal

losses. The algorithm used here first determined the excitation which produced the highest

water resonance amplitude (ie. the least non-linear motion) for each of the 100 individual echo

times. Results for the other seven excitations were then scaled to match this water resonance

amplitude. In the absence of motion, the water resonance amplitudes for all 8 NEX had a

comparatively small noise envelope and thus the streak correction had negligible impact. When

non-linear motion was present, however, the algorithm substantially reduced the spurious

fluctuations and subsequent streak artifacts in the 2D FID. The water resonance was then

removed using Hankel singular value decomposition (HSVD). The signal was then Fourier

transformed and automatically phase corrected for zero and first order phase terms118 which

were obtained from the water resonance prior to removal, inverse Fourier transformed and

90

Figure 4.2: Flow chart of the processing steps used to estimate ADCs from the raw DW-JPRESS data.

The 2D free induction decay (FID) results are processed through two separate pipelines referred to as

the 2D pipeline (left) and the 1D pipeline (right). The arrow connecting the 1D pipeline and the 2D

pipeline indicates that the 1D Cr303 data are multiplied with the results from ProFit (because Profit

outputs spectra as a ratio to Cr303), ensuring that the diffusion characteristics of Cr303 do not bias ADC

estimates for all metabolites. The preprocessing steps are done for each TE value (ie. 100 times) with

two repetitions: once for high diffusion and once for low diffusion. This produces two 2D FIDs which are

then combined to calculate the ADCs in the bottom portion of the flow chart.

91

averaged over all NEX. These preprocessing steps were repeated for each echo step (100 times)

for the data obtained at both high b-value and low b-value.

The preprocessed FIDs were then submitted to two spectral analysis pipelines,

subsequently referred to as the 2D and 1D pipelines (left and right columns in Figure 4.2,

respectively). The 2D pipeline consisted of analysing the 2D spectra at both diffusion weightings

with ProFit, using a basis set simulated from the complete density matrix with the General

Approach to Magnetic resonance Mathematical Analysis (GAMMA) library155 using the same

metabolites as in the original ProFit implementation14 minus glucose (due to its very small

concentration). The basis set consisted of 19 metabolites: alanine (Ala), ascorbic acid (Asc),

aspartate (Asp), Cr, phophorylcholine (PCh), GABA, Gln, Glu, glycine (Gly), glutathione (GSH),

glycerophosphorylcholine (GPC), lactate (Lac), myo-insitol (mI), NAAG, phosphorylethanolamine

(PE), scyllo-inositol (Scy) and taurine (Tau). For the work presented here, it was found that

there was insufficient spectral resolution to separate NAAG from NAA and PCh from GPC. For

this reason, the results from NAAG and NAA were summed and subsequently referred to as

total NAA (tNAA), and the results from PCh and GPC were summed and referred to as total

choline (tCho). Additionally, Cr was split into two separate metabolites, referred to as Cr303

and Cr391 for the two resonances at 3.03 ppm and 3.91 ppm, respectively, as is typical with

ProFit14.

Because ProFit results are output as a ratio to the Cr303 peak, the 2D data were then

multiplied by the Cr303 value obtained from the 1D pipeline (represented by the horizontal

arrow connecting the two pipelines in Figure 4.2) to estimate ADCs using unscaled

concentration values rather than ratios to Cr303. This processing step was important to remove

bias, as otherwise the 2D spectral results at each b-value would be weighted by the specific

diffusion characteristics of Cr303. The estimated diffusion characteristics for Cr303 were thus

identical for both the 1D and 2D pipelines. The 1D pipeline consisted of separately analysing the

data for each of all 100 TE steps with the freeware known as Totally Automatic Robust

quantitation in NMR (TARQUIN94). Because TARQUIN requires the onset of data acquisition to

92

occur at the echo maximum, all data acquired prior to the peak of each spin echo were

discarded for this analysis. This corresponded to truncating the first ( ) samples for the

kth line in the raw 2D FID. The average of the metabolite values obtained over the 100 individual

lines was then used to estimate the 1D pipeline ADCs. The basis set was automatically

generated in TARQUIN and contained identical in vivo metabolites to the basis set used in the

2D pipeline, as described above.

In preliminary work, DW-JPRESS experiments were also conducted on the GE Healthcare

"BRAINO" phantom that contains a restricted set of metabolites with known concentrations133.

In these cases, the basis set for the 1D and 2D pipelines was restricted according to a priori

knowledge of the metabolite set (Cho, Cr, NAA, mI, Lac and Glu) to avoid overfitting. The 1D

pipeline was also used for estimating the in vivo ADC values of NAA, Cr, Cho, mI and Glu. These

results provided a useful comparison with the analogous estimates obtained from the 2D

spectral analysis.

The ADCs were estimated for both pipelines according to the standard equation

[ ( ) ( )]

(4.1)

where ( ) is the peak area value obtained from the fitting software for each particular

metabolite, m, from the spectra with b-value , and ( ) is the analogous metabolite peak

area value from the signal with b-value . The uncertainties of ADC parameters estimated by

the 1D pipeline were obtained by propagating the uncertainties in the concentration estimates

from the 100 individual TE values. For the 2D pipeline, the analogous uncertainties were

obtained from the uncertainty of the Cr303 values estimated by the 1D pipeline and the

Cramer-Rao lower bounds outputted by ProFit, according to standard error propagation. The b-

values were calculated according to the equation for a trapezoidal gradient:

[ (

) ]

(4.2)

93

where is the gyromagnetic ratio for protons, and is the amplitude of the DSG at the first TE

value. Because each TE step resulted in a different duration between the two DSGs, it was

necessary to adjust the amplitude of the DSGs at each TE value to keep the b-value constant.

This was achieved by the following equation:

√ (

)

( )

(4.3)

where is the amplitude of the DSG at the kth TE step. The b-values were 2188 and 22 s/mm2

(G1 amplitude of 50 mT/m and 5 mT/m) for the high b and low b spectra for in vivo

experiments, respectively. For the phantom experiments, the four b-values were 1012 s/mm2,

1264 s/mm2, 1544 s/mm2 and 1852 s/mm2 (G1 amplitude of 34 mT/m, 38 mT/m, 42 mT/m and

46 mT/m), respectively, reflecting the change in material properties. A smaller maximum b-

value was used in the latter case because the ADC values were known to be substantially higher

in the phantom than in vivo. For the same reason, a larger minimum b-value was used to assist

in water suppression in the phantom. To investigate the efficacy of using Equation 4.3 to

modify the amplitude of the DSGs for each TE step, ADCs were calculated and compared for the

first 15 and last 15 TE values in the phantom experiment.

To investigate the potential for bias between the two data processing pipelines, the

ADCs from both pipelines for tNAA, tCho, Glx and mI were compared using a Mann-Whitney U

Test, with the threshold for statistical significance set according to a Type 1 error of .

Additionally, to quantify the deviation between the two pipeline ADC estimates for tNAA, tCho,

Glx and mI, the root mean square relative difference, for each metabolite, m, was

calculated:

√

∑(

)

(4.4)

94

where and are the estimates from the 2D and 1D pipeline for metabolite

m and the ith subject, respectively (N subjects in total). This value was not calculated for Cr303

because the values from both pipelines were identical, as described above.

Once the DW-JPRESS pulse sequence was debugged and validated using the “BRAINO”

phantom, experiments were subsequently conducted in 8 young healthy adult volunteers free

from previous or existing neurological or psychological deficits. All volunteers participated with

free and informed consent and with the approval of the Research Ethics Board at Sunnybrook

Health Sciences Centre. One dataset was discarded due to poor shim, and another due to

motion that resulted in negative ADCs. Thus the data for 6 volunteers are presented (one

female) with an age range of 23 to 35 years. For the phantom experiment, the voxel was placed

within the middle of the “BRAINO” phantom and was 2.53 cm by 2.67 cm by 2.44 cm in size

(16.5 cm3). In vivo, voxels were consistently placed in predominantly parietal white matter with

sizes that ranged from 4.76 cm by 1.86 cm by 2.29 cm (20.3 cm3) to 4.54 cm by 2.78 cm by 2.54

cm (32.1 cm3) in the anterior/posterior, right/left and superior/inferior directions, respectively.

Figure 4.3 shows a representative voxel overlaid on top of axial and coronal anatomical images.

The total experiment time for each subject was approximately 75 minutes, which included a

localizer and T1-weighted anatomical imaging (FSPGR IR, 256 by 256 pixels, pixel size = 0.86 mm

by 0.86 mm, TR/TE = 8.2/3.2 ms, flip angle = 8°). Each individual water-suppressed JPRESS

spectra was acquired in approximately 26 minutes, although due to the cardiac gating

procedure, this depended on the heart rate of the subject.

4.3 Results

Table 4.1 lists the ADCs estimated from the “BRAINO” phantom using both the 1D and 2D

pipelines. The measured temperature within the bore of the magnet was 19°C and, for

comparison with literature values typically given at 20°C, the data from the phantom were thus

scaled assuming equal activation energy between water and the metabolites156 using cubic

interpolation of the water ADC values from Sacco et al.157 The values listed in Table 4.1 include

an increase of approximately 3 % above the unscaled values due to this temperature correction

95

Figure 4.3: a) Axial prescription and b) coronal prescription of the DW-JPRESS voxel for Subject 3. DW-

JPRESS spectra for this subject are displayed in Figure 4.4.

factor. The ADC of water, as estimated from the residual water signal after water suppression,

yielded a value corrected to 20°C of x10-3 mm2/s using four b-values. The

analogous corrected value was x10-3 mm2/s using the lowest and highest b-values

only, as subsequently used in vivo. These estimates agree well with each other and the ADC

values reported in the literature for water at room temperature, which range between 1.95

x10-3 mm2/s and 2.1 x10-3 mm2/s101,102,108,157. Table 4.1 also shows excellent agreement

between ADC estimates for both pipelines for the three metabolites typically measured by DW-

MRS: Cr (both components at 3.03 and 3.91 ppm), choline and NAA. The measured percent

difference of the ADC estimated from the first 15 TE values compared to the last 15 TE values of

the 2D data set, using the b-values of 1012 s/mm2 and 1852 s/mm2, was ( ) %,

( ) %, ( ) %, ( ) %, ( ) %, ( ) % and

96

( ) % for Cr303, Cr391, tNAA, Cho, Lac, Glu, and mI, respectively. Negligible

differences were

Table 4.1: ADC estimates obtained from the “BRAINO” phantom for the 2D and 1D pipelines scaled to

20°C. For each pipeline, results are listed for fitting data acquired at 4 b-values (1012 s/mm2, 1264

s/mm2, 1544 s/mm2, 1852 s/mm2) to Equation 1 using linear least squares, as well as for fitting with 2 b-

values (1012 s/mm2, and 1852 s/mm2).

Metabolite 2D-pipeline ADC (x 10-3 mm2/s) 4 b-values

2D-pipeline ADC (x 10-3 mm2/s) 2 b-values



Cr303

Cr391

NAA

Cho

Glu

Lac

mI*

*Because of rapid T2 relaxation, only the first 10 TE steps were used in this case.

observed between ADC pairs in all cases except Cr391, verifying that it was acceptable to use

Equation 4.3 to maintain a constant b-value for all TE values. It can also be seen Table 4.1 that

the ADCs estimated using two b-values are in excellent agreement to those obtained using 4 b-

values for both pipelines, validating the use of two b-values for the in vivo experiments.

Figure 4.4 shows typical in vivo 2D JPRESS spectra obtained with both high b-values and

low b-values, including the spectral fitting and residual results obtained from ProFit. Similar fit

quality is obtained in both conditions, indicating that the large DSGs have negligible impact on

the quality of the spectral data (eg. linewidth).

Table 4.2 lists the ADCs estimated from both pipelines for 6 healthy volunteers. The mean was

calculated using only values which were physically realistic (ADC value 0.80 x 10-3 mm2/s, the

mean ADC of water in brain measured here, which the metabolites should not surpass, and

0.09 x 10-3 mm2/s, which is the lowest measured ADC in healthy tissue of any metabolite

previously reported109). Some of the weakest resonances were not reliably quantified by ProFit

(as observed by extremely large Cramer-Rao bounds or by non-physical

97

Figure 4.4: Water-suppressed JPRESS spectra obtained from Subject 3 with voxel prescription as shown

in Figure 4.3. The real spectra (top), fits (middle) and residuals (bottom) are plotted on a logarithmic

color scale. High b value weighting is shown in the left column of plots, with low b value weighting

shown in the right column. Note that due to the scaling performed by ProFit the amplitude appears to

be the same; however, the high b spectrum has its signal amplitude reduced by ~35 %. As can be seen

from the comparable residuals, similar quality fits are obtained for both low and high b-values. The

subfigures are logarithmically scaled and window and levelled consistently throughout, with negative

values depicted in blue.

negative ADC values) and were therefore excluded. The metabolites Ala, Asp, Asc, Pe, GABA,

Gln, Gly, and Tau were found to have physically unrealistic ADCs (either negative or several

times larger than water) for at least one subject. As expected, a wide range in ADC estimates is

observed across the remaining resonances, with the highest mean ADC observed for Tau

(although this result may not be reliable), and the lowest for mI and tCho. These results suggest

that knowledge of the ADC values for some of these other metabolites beyond Cr, tNAA and

tCho may provide additional valuable information.

98

Table 4.2: ADCs estimated from 6 subjects for 2D and 1D pipelines.

Metabolite

Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6 Mean

2D Pipeline ADC (x 10-3 mm2 /s)

Cr303

Cr391

tNAA

tCho

Ala -

- - - -

Asp

Asc

GABA -

-

Glu

Gln

Glx

Gsh

Gly

-

Lac -

-

mI

Pe

- - - -

Scy

Tau -

-

1D Pipeline ADC (x 10-3 mm2 /s)

Cr303

tNAA

tCho

Glx

mI

Water

99

No significant difference is observed between the median ADC values estimated from

the two pipelines for tNAA (P = 0.82), tCho (P = 0.39), Glx (P = 0.82) and mI (P = 0.70). Figure 4.5

demonstrates the correlation between the 1D and 2D pipeline estimates for tNAA, tCho, Glx, mI

across the six subjects. The measured Drms,m values were ( ) %, ( ) %,

( ) %, and ( ) %, for tNAA, tCho, Glx and mI, respectively. The larger

variation for Glx and mI, as well as reduced precision, is likely due to the difficulties in

estimating these resonances using traditional 1D spectroscopy.

Figure 4.5: Plot of ADCs estimated from the 2D pipeline versus those estimated from the 1D

pipeline. Strongest agreement is observed for tNAA, followed by tCho; poorest agreement is

observed for Glx and mI.

100

4.4 Discussion

The quality of the spectra shown in Figure 4.4 was obtained by carefully controlling for the

effects of eddy currents. A previous study found that a single water unsuppressed spectrum

was sufficient for eddy current correction in typical JPRESS spectra16 because the majority of

the eddy currents arose from the last set of gradients applied. However, preliminary

experiments related to the present work (not shown here) found that water-unsuppressed

spectra acquired at a single TE value were insufficient to remove eddy current effects in DW-

JPRESS spectra for all TE steps, as apparent by distortions in the lineshapes. This is likely due to

the fact that substantial eddy currents result from the very large DSGs, which have modified

timing for each TE step. Differences in gradient coil hardware characteristics between the

research MRI system used here and that used in the previous work16 could also be an important

factor. Irrespective of the cause, one full water-unsuppressed JPRESS spectra was acquired for

each b-value to correct for eddy currents with good efficacy across all TE steps. The eddy

current correction was done for each individual coil element prior to averaging, as shown in

Figure 4.2, because in principle each may be sensitive to slightly different eddy currents.

Due to its simplicity, Equation 4.3 was used at each TE step to calculate the amplitude of

the DSGs. This equation neglects the small effect of crushers, however, which at the larger TE

values produce a slightly larger b-value than the nominal b-value listed. The b-value for each

echo step was also calculated using the alternate approach of integrating the square of the k-

space trajectory mapped out by the gradient waveforms, which included all DSGs and crushers.

It was found that the estimate of the b-value through this technique was approximately 0.5 %

greater for the last echo than the first echo, indicating a negligible change for the precision of

the experiments presented here. This was corroborated by the negligible changes observed in

the phantom experiment when comparing ADC values calculated from the first 15 TE values to

those calculated from the last 15 TE values. As part of these observations, the very large

uncertainties in the ADC difference values for Glu and mI are due to the relatively low

concentrations as well as the low T2 values of these metabolites in the phantom (resulting in

large variations in the ADCs estimated at larger TE values), whereas the large uncertainty

observed for Cr391 is due to contamination from the water peak, as explained further below.

101

It is also important to emphasize that the present work does not provide rotationally

invariant estimates of ADCs, as DSGs were applied in all three orthogonal directions

simultaneously. The rotationally invariant estimate is usually achieved by acquiring diffusion-

weighted spectra from gradients successively applied in three orthogonal directions, but this

was impractical in the present work due to experiment time constraints and the need to

generate high b-values while keeping the TE values relatively low.

The initial DW-JPRESS data from the BRAINO phantom (Table 4.1) confirmed that both

the 1D and 2D pipelines produced ADC estimates in good agreement with each other for all

metabolites except mI. The discrepancy observed for mI was likely due to quantification

difficulties using 1D spectroscopy, and the small T2 value of this metabolite in the phantom. The

results also agree with ADC values previously estimated from an identical phantom as

x10-3 mm2/s, x10-3 mm2/s , and x10-3 mm2/s for tNAA,

creatine and choline, respectively, using a DW-MRS PRESS sequence at 7 T100, whereas the

values obtained from the 2D pipeline were x10-3 mm2/s, x10-3 mm2/s

and x10-3 mm2/s. The previously measured ADC of glutamate within a glutamate-

only phantom was x10-3 mm2/s at 7 T108, which is about 20 % larger than the value

presented here of x10-3 mm2/s for the two b-value estimate. This discrepancy

could be due to differences in the temperature at which these values were measured,

differences in eddy current correction schemes or other differences in the data processing

pipelines between studies, such as the confounding overlap of glutamate and NAA at 2.03 ppm.

For the other metabolites (NAA, Cho and Lac) the difference between the ADCs values

estimated by both pipelines is comparable to the experimental uncertainty. The 2D pipeline

provides substantially improved precision, however, likely because of the improved fitting

capability that arises from introducing a second spectral dimension. Overall, these results

indicate that the 2D pipeline can be used to extract ADC estimates in agreement with those

obtained at higher field strengths in phantom experiments. Due to the difficulty in quantifying

smaller resonances using 1D MRS at 3 T, no attempt was made to quantify ADCs beyond tNAA,

Cr, Cho, Glx and mI using the 1D pipeline for in vivo data (which exhibit much broader

linewidths and lower SNR than the phantom data).

102

Previously reported ADC values for tCho, tNAA and Cr in white matter108 are

x10-3 m2/s , x10-3 mm2/s and x10-3 mm2/s, respectively. The

mean values for tCho, tNAA and Cr303 from the 2D pipeline used in the present work are

mm2/s, mm2/s and mm2/s, respectively. The present

mean ADC value estimated for tCho is slightly elevated compared to the previous finding.

However, the results between 2D and 1D pipelines are in excellent agreement for all subjects

except Subject 1, for which the tCho estimate for the 2D pipeline was slightly elevated,

although this was still within experimental uncertainty. This observation notwithstanding, no

statistically significant differences were observed between the two pipelines for tNAA, tCho, Glx

or mI ADC estimates across all subjects (Cr was not tested because it was identical for the 2

pipelines, as described in the Methods). There is, however, an increase in the standard

deviation across all 6 patients from the 2D pipeline as compared to the 1D pipeline. This is due

to the uncertainty on the 2D ADC estimates being derived from the uncertainty from both the

1D pipeline Cr303 and the uncertainty on the 2D individual metabolites from ProFit. It should

also be mentioned that due to the inherent variation in diffusion time as well as TE value within

this DW-JPRESS, possible correlations between relaxation and diffusion-weighted signals may

impact the estimated ADC values. It has previously been suggested that no correlation between

relaxation and diffusion properties exist for mouse brain158, however a significant correlation

was found for NAA and Cr within human brains159 (although the effect was small). The present

results are also consistent with a small effect, given that the estimated ADC values agree well

with previous estimates obtained with standard 1D DW-MRS. Nevertheless, mitigation

strategies should be considered for future studies (such as use of a smaller value for ).

Future work will investigate removing the Cr303 scaling by ProFit, which in turn will

make it unnecessary to perform the processing step of multiplying DW-JPRESS results by Cr303

values obtained from the 1D pipeline. The net result should be improved precision on the ADC

values estimated by DW-JPRESS. The estimated ADC of water obtained here across all six

subjects of x10-3 mm2/s agrees well with the previous invariant trace/3 ADC

estimate in healthy white matter at 3 T102 of x10-3 mm2/s. The values obtained

within the present work for Cr, NAA, Cho lie in the range previously reported for rat

103

brain146,147,160–163. However, The estimates obtained here for mI, Gln and GSH (which reside in

glial cells) and Glu (which resides in axons) are elevated compared to rat studies146,147,

suggesting a possible differences in the compartments within the respective cells, physiological

differences between species, or differences in acquisition and analysis schemes. Furthermore,

the effects of restricted diffusion have been completely neglected in the present work due to

the minimum number of b-values chosen, as well as the relatively small maximum b-value used.

Future studies may separate the fast and slow-diffusing components using more b-values and a

biexponential fit, which can be used to estimate the intra-extracellular distribution of

metabolites, as has previously been done in rat brains147.

Possible causes of the slightly elevated tCho ADC estimates in the present work,

compared to previous reports, include residual errors from cardiac pulsatility and T1 recovery.

The effects of cardiac pulsatility were elevated by use of a somewhat large MRS voxel to

improve SNR in DW-JPRESS. Although the "streak-removal" procedure was effective at

suppressing pulsatility effects that remained present even in the presence of cardiac gating,

close inspection of the data indicated that a small amount of pulsatility artifact still remained.

In addition, cardiac gating introduced a variability in the TR interval that created small signal

fluctuations related to differences in the extent of T1 recovery for each of the resonances. The

former issue can be addressed by moving to a smaller voxel (see below) or, for example, by

implementing navigator-based reacquisition of corrupted data100. The T1 recovery effect can be

addressed by increasing the diffusion-weighting, which would likely improve the ADC precision,

or by increasing the TR so that small variations in TRs have less impact, at the expense of

increased experiment time. It is possible that tCho was most affected by small variations in TR

values because it exhibits a shorter T1 value than Cr or tNAA119. It is also important to mention

that the present work is particularly sensitive to bulk tissue motion due to the relatively long

experiment time, and the use of large diffusion sensitizing gradients to detect molecular

displacements. It is likely that this sensitivity can be reduced by future technical development,

for example by implementing a new version of the DW-JPRESS pulse sequence optimized for

newer MRI systems with enhanced SNR and gradient amplitude. Additionally the voxel used

was predominantly white matter, as it allowed for the largest voxel possible, although due to

104

the relatively higher concentration of metabolites in grey matter than white matter in future

implementations it may be possible to increase the SNR by moving to a smaller voxel placed in

grey matter.

Previously reported ADC values for Glu, Gln and Glx (Glu+Gln) in parietal white matter of

healthy volunteers are x10-3 mm2/s, x10-3 mm2/s and x10-

3 mm2/s, respectively, using a DW-MRS PRESS sequence at 7.0 T108. For comparison, the

present work obtained values of x10-3 mm2/s, x10-3 mm2/s and

x10-3 mm2/s, respectively, in good agreement. The one notable exception involved

Subject 5, for which the ADC estimated for Gln was uncharacteristically low. This anomalous

result was likely due to the inability of ProFit to distinguish between the two glutamate-

containing moieties. Thus, the reliability of DW-JPRESS to separate Glu from Gln reliably at 3 T

remains an open question and likely depends on the available SNR as well as shim quality.

Additionally, the value for Glu obtained here agrees well with a previous value of

x10-3 mm2/s which was obtained in a monkey brain using Carbon-13 labelled glutamate164.

The 2D pipeline ADC estimate for mI was x10-3 mm2/s, in good agreement

with previous results obtained in an anaesthetized monkey165 ( x10-3 mm2/s), and in

healthy volunteers using diffusion-tensor spectroscopy145 ( x10-3 mm2/s).

Interestingly, the ADC value for mI was among the lowest of all metabolites that were studied

(along with tCho). It was also observed that the ADC of mI (which primarily resides within glial

cells) was smaller than Scy, despite an equal molecular weight. This suggests that there is a

potential difference in either cellular localization or compartmentalization of these molecules.

This difference was close to statistically significant (P = 0.06). Although the estimate for the ADC

of Scy was anomalously high for Subject 1, even when excluding this subject a difference trend

was observed between the ADC estimate for Scy and mI (P = 0.08). There was a weak

correlation (R2 = 0.13) between the ADC and the square root of the molecular weight. This

indicates that although some of the variation in the ADCs can be attributed to size of the

molecules, most is due to various compartmentalization factors.

105

The remaining metabolites of interest were probed less reliably by the prototype

implementation of DW-JPRESS. Only Subjects 3 and 4 had a coefficient of variation below 25 %

for GABA. The T2 of GABA is approximately 88 ms166, similar to the initial TE value of 74 ms

which ensures appreciable T2-weighting of the DW-JPRESS results. Furthermore, GABA has a

relatively low concentration and strong overlap with larger resonances. The ADC values for Ala,

Pe and Tau were not reliable in any subjects, likely for similar reasons. Future work to reduce

the minimum TE value could help to alleviate this problem (using an MRI system with higher

gradient strength) and allow ADCs to be estimated reliably for a larger range of biomolecules

than was achieved in the present work. It would also be interesting to investigate the

implementation of DW-JPRESS at 7 T where there is improved SNR and decreased overlap

between resonances.

In principle, identical ADC values should be estimated for Cr303 and Cr391 using the 2D

pipeline. The results listed in

Table 4.2 show consistently higher in vivo values for Cr391 than for Cr303, however (except for

Subjects 5 and 6 where the estimate is markedly low). This effect likely arises from use of the

HSVD method to remove water signal, which affects the Cr391 resonance because it is near the

HSVD cut-off frequency. Conversely, the phantom experiments provided very similar ADC

estimates for Cr303 and Cr391 for both pipelines because of substantially reduced linewidths in

comparison to in vivo conditions, and thus reduced effect from water contaminating the Cr391

peak.

A relatively large voxel size was used in this proof-of-principle work, even in the context

of single voxel experiments. In general, JPRESS requires a large voxel size for accurate detection

of the smaller coupled resonances, and the diffusion-weighting introduces an additional

reduction in SNR of ~35%. The b-value used for the in vivo measurements was 2188 s/mm2,

whereas the b-value that minimizes the standard deviation of Equation 4.1 is approximately

80% of 1/ADC. It is therefore possible that the optimal b-value which minimizes the expected

uncertainty on the ADCs is somewhat higher, which is corroborated by Ellegood et al.167

although care must be taken due to the potential of increased weighting from the restricted

106

diffusion regime at higher b-values. With the gradients used here this would necessitate

increasing the gradient duration, , which would increase TE values, increase the effects of

eddy currents and bring the sequence further away from the q-space condition of

instantaneous diffusion, resulting in artificially inflated ADCs. Improvements can likely be

obtained by optimizing diffusion sensitization, choice of TR as well as the number of TE steps.

Typical 1D DW-MRS has previously been optimized at 3 T, where a recommendation of 13

minutes duration was prescribed105. A similar reproducibility study should be done to

determine optimal DW-JPRESS measurement parameters to obtain reliable ADC estimates with

low variability. This will be investigated in the future, and it is likely that optimized

implementations of DW-JPRESS can significantly reduce the total scan time or voxel size while

keeping a similar precision on the ADCs obtained here. In addition, bipolar gradient schemes109

have been shown to reduce eddy currents108 and their implementation should be investigated

for DW-JPRESS.

In principle DW-JPRESS could be combined with parallel imaging to measure the change

in ADCs in two or more voxels simultaneously138, which would be critical in reducing experiment

times for future DW-MRS applications such as those that compare results between healthy and

diseased tissues. However, care must be taken to avoid the possibility of introducing artefacts

from a multivoxel reconstruction.

4.5 Conclusions

A novel technique which combines JPRESS with DW-MRS was developed to measure the ADCs

of metabolites beyond NAA, Cr and Cho at 3 T. The proposed technique was found to provide

consistent estimates for the ADCs of tNAA, Cr and tCho when compared to a typical DW-MRS

pipeline. Additionally the new technique provided realistic estimates for the ADCs of glutamate

+ glutamine, and myo-inositol in all subjects and additionally glutathione and scyllo-inositol in

all but one subject. With further technical development to address the main limitation of long

acquisition times, DW-JPRESS will become more practical and may provide useful information

about the diffusion characteristics of metabolites beyond NAA, Cr and Cho at 3 T.

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Chapter 5

Conclusion

The most common in vivo MRS sequences are PRESS4 and STEAM5 which were developed

almost 30 years ago and, in principle, remain identical to their initial inception. Despite its

mature status, however, MRS continues to find new applications due to its underlying ability to

non-invasively measure physiological information to supplement anatomical information from

MRI. What follows is a discussion of the outcomes of the thesis, as well as recommended future

work. It is evident that there remains considerable scope for further development of in vivo

MRS technology. It will be interesting to see how the field of in vivo MRS evolves as the

translation of in vitro techniques becomes increasingly possible, due to ongoing improvements

in hardware such as increased field strength, improved high order shimming, and increased

gradient amplitude and slew rate.

5.1 Summary

In Chapter 1, a brief overview was presented of the necessary physics to understand the work

developed in the thesis. In particular, the quantum mechanical basis for proton MRS was

explained and the product operator formalism was introduced. The basics of in vivo MRS were

explained including the metabolites observable in the brain, how the signal is spatially localized,

as well as topics such as parallel imaging, absolute MRS and diffusion-weighted MRS, that were

relevant for the work that was undertaken. In addition, the present status of MRS as applied to

brain cancer and brain cancer treatment was also discussed.

Within Chapter 2 a study was presented which extended SVS to include multiple voxels

through amplitude modulation of the excitation RF pulses and use of localized multi-channel

coil sensitivity to reconstruct the individual voxels. By this approach, referred to as “constrained

source space MRS” (CSSMRS), it was shown that high quality data from two voxels, with

relatively few artefacts and very little “bleed” from one voxel to another. In addition, the

increase in noise due to the geometry of the head coil was investigated as a function of

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distance between the two voxels, which was necessary to determine whether there is a time

benefit in using CSSMRS. Based on the positive results of this experiment, the CSSMRS

technique was applied to a cohort of brain cancer patients. This was undertaken because SVS is

often used in that particular clinical setting to differentiate between neoplastic and non-

neoplastic lesions, typically with additional data acquisition from a contralateral brain region as

a control. Positive results were again obtained and it was concluded that CSSMRS could

potentially halve the duration of clinical MRS protocols where spectra are measured from both

lesion and control locations.

Within Chapter 3 a novel pulse sequence was developed which was a hybrid of fast IR

and saturation recovery used it to estimate the longitudinal relaxation time of lactate, filling a

gap in the in vivo MRS literature. The sequence was similar to a standard IR experiment except

that the inversion pulse was interleaved on and off successively, and the total repetition time

(TR) was linked to the inversion time (TI). By making these two changes: 1) the difference signal

at each TI value followed a monoexponential function of T1, enabling simple fitting procedures

to estimate the relaxation time; and 2) the consistent need for long TR values (as required in

standard IR) was removed, providing increased time efficiency. The predicted uncertainty on

the estimated T1 of lactate was then numerically minimized by finding the optimal TI and TR

pairs to use within fixed measurement time. Spectral editing pulses were used to separate

lactate from the contaminating lipid signals and the first lipid-free estimates of lactate T1 were

thus reported in vivo. From specific validation experiments in phantoms, the technique was

found to provide approximately 25 % improvement in measurement precision over standard IR.

The mean lactate T1 value was subsequently determined to be ( ) ms over a group

of 6 patients with high grade glioma.

In Chapter 4 a novel technique was developed which combines a diffusion-weighting

module with the 2D JPRESS technique. Typical DW-MRS experiments at 3 Tesla only investigate

the diffusion of NAA, choline and creatine due to their relative ease of measurement. JPRESS,

however, allows for resolving smaller J-coupled resonances because of the introduction of a

second spectral dimension. By combining these two techniques a more accurate measurement

of the diffusion characteristics of glutamate + glutamine, myo-inositol, glutathione and scyllo-

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inositol was obtained. Good agreement was observed between the two techniques estimates

for the typical metabolites diffusion characteristics, namely NAA, creatine and choline in both

phantom and healthy volunteer experiments. Optimization of this technique may allow for the

estimation of additional metabolites beyond those mentioned above.

From these collective research milestones, multiple avenues of future research are

possible. Several examples are provided in the following sections.

5.2 Future Directions for CSSMRS

The CSSMRS work developed in this thesis focused exclusively on acquired spectral information

from two voxels simultaneously, as it was the simplest extension of SVS. However, extension to

a larger number of voxels is desirable in certain applications, such as when multiple disease foci

are present (eg. brain metastases). There are two main issues to address when extending

CSSMRS beyond two voxels: 1) how to perform appropriate spatial localization; and 2) whether

√ advantages are maintained due to the increase in noise from the condition number

of the reconstruction matrix. Considering the first issue, the simple localization scheme

presented in Chapter 2 does not enable additional voxels to be positioned arbitrarily. One

alternative is to make a small pulse sequence modification to cosine modulate one of the

refocusing RF pulses, which then enables four voxels to be localized. Three of these four voxels

can then be arbitrarily positioned (subject to the condition that the three voxels cannot lie

along a single line), although the position of the fourth voxel becomes very constrained. This

was implemented in preliminary experiments, as shown in Figure 5.1. This approach could also

be extended further by modulating both of the refocusing pulses, which would allow four out of

a total of eight voxels to be arbitrarily localized (subject to the condition that the four voxels

cannot lie in one plane).

In principle this implementation is straightforward, but the cosine modulation has the

effect of reducing the bandwidth of the pulses by a factor of two (assuming the refocusing

pulses are played out at the physical maximum allowed B1 amplitude). The bandwidth

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Figure 5.1: Four voxel profile overlaid on an anatomical axial MR image. Three of the four

voxels were positioned and the fourth voxel was localized based on an algorithm that made the

voxels as square as possible.

reduction results in greater chemical shift displacement and, for J-coupled chemical species, a

reduction in the measured signal amplitude due to partial cancellation at the borders of the

voxel. A more robust solution is the arbitrary excitation of multiple voxels through the

excitation k-space approach72. Mapping out excitation k-space is typically quite slow, however,

so it is likely that a parallel excitation approach utilizing an array of transmit coils168 such as

Transmit SENSE169 (also known as “parallel RF transmission”) is needed to reduce the duration

of the excitation pulse.

Considering now the second issue, acquiring more voxels with CSSMRS while using the

same multi-channel receiver coil is expected to increase the g-factor, and because CSSMRS is

only beneficial when the g-factor is less than the square root of the number of voxels, future

multi-voxel implementations will need to investigate the g-factor dependence in detail. The g-

factor increase relates to the increase in the condition number of the matrix ( ) in

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Equation 2.5 as the number of voxels increases, due to the increase in overlap of the signal

measured by each coil. In principle, the g-factor can be improved by increasing the number of

coil elements and reducing the sensitive volume of each element. Coil channel count can be

increased in a reasonably straightforward manner from the 8-channel configuration

investigated in Chapter 2, as head coils with up to 64 channels are now commercially available.

Additionally the sensitivity profile of coils at higher static magnetic field strengths (e.g., 7 T)

typically varies more steeply which will result in a reduction in g-factor.

There are also potential advantages to combining the CSSMRS technique with 2D

JPRESS, as the alternative of combining 2D JPRESS with MRSI is very challenging. For example,

2D JPRESS usually acquires between 1 and 8 repetitions (TR intervals) for signal averaging at

each TE step, whereas an MRSI acquisition with 8 by 8 voxels (as is often used) requires at least

64 TR intervals for spatial encoding. For a typical 1D MRS experiment at a single TE value, 64 TR

intervals is acceptable and leaves some experiment time for signal averaging. For a 2D JPRESS

experiment, however, 64 TR intervals per TE step leads to an experiment time of ~3 hours.

Instead, CSSMRS could easily be applied to JPRESS to permit acquisition of 2D spectra from two

(or possibly more) separate voxels simultaneously. There are some artefacts introduced from

the CSSMRS approach138,139, and so a careful investigation of how CSSMRS impacts the ability to

extract signals from brain metabolites present at small concentrations is needed.

5.3 Future Directions for DW-JPRESS

Before DW-JPRESS is applied to investigate changes in the diffusion of metabolites associated

with neuropathology, there is substantial technical optimization that could be performed on

the pulse sequence. In particular, it will be important to reduce the echo time, as well as a

combination of (1) the total scan duration; 2) the voxel size; and most importantly 3) the

variability of the estimated ADCs. There is an inherent tradeoff between these latter three

factors, which must be considered carefully. Given the long experiment times associated with

the current implementation of DW-JPRESS in Chapter 4 (75 minutes), these pulse sequence

optimizations are preferable before progressing to clinical research applications.

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Reducing the minimum TE value has the effect of increasing the SNR in DW-JPRESS

(substantially for some of the interesting metabolites such as GABA, which has a T2 of 80 ms166).

Eddy currents, which are dependent on the gradient plateau duration, , will also be reduced.

The minimum TE value used in Chapter 4 was 74 ms, which can be greatly reduced by moving to

an MRI system with higher maximum gradient amplitude, either in a whole-body or head insert

gradient configuration. Additional reduction of the minimum TE value is also potentially

possible by increasing the gradient slew rate, which in the present work was kept to a quarter

of its maximum value to reduce vibromechanical motions that can corrupt diffusion-weighted

measurements. Preliminary experiments found that there was substantial table motion when

the MRI system used in this thesis was run with a higher slew rate. Assuming vibromechanical

effects can be overcome, using the maximum slew rate available on whole-body MRI systems

(200 T/s/m) and a maximum gradient amplitude of 80 mT/m (a factor of 2 increase compared

to the hardware used in Chapter 4) theoretically it should be possible to reduce the minimum

TE value to ~60 ms while keeping the same b-value. This is a larger minimum echo time than is

typically used for DW-MRI due to the reduced ADCs of metabolites as compared to water.

Nevertheless this reduction in echo time could improve SNR by ~15% for the fast-decaying

metabolites such as GABA. The minimum TE value could be reduced even further by including a

bipolar pair of gradients between the two refocusing pulses.

Furthermore, the choice of TE step (chosen in Chapter 4 to be 2 ms, consistent with

previous JPRESS implementations), and TR could be optimized for a particular metabolite of

interest (provided both the T1 and T2 are known). Increasing the TE step has the effect of

greater spectral resolution in the J-resolved direction, which will increase separation of

metabolites. However the increase in spectral resolution will reduce the SNR significantly due

to increased T2-weighting. Thus there is some optimal TE step that provides the minimum

measurement uncertainty for a given scan time. In principle a numerical optimization

procedure should be undertaken analogous to that of Chapter 3, that includes all of interwoven

experimental factors to minimize the expected uncertainty on the estimated ADC of one (or the

average of multiple) metabolites of interest with the uncertainties on individual metabolites

estimated using Cramer-Rao Lower Bounds (CRLBs)170,171. It has previously been shown that the

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optimization of CRLBs in a typical JPRESS experiment had a reduction in the coefficients of

variation for Glutamate of approximately 30 %171. Thus, combining optimal pulse sequence

parameters with increased maximum gradient amplitude and slew rate should allow reduction

in the total experiment time by a factor of two or more while maintaining reliable

measurements of metabolite ADCs. Additionally, by moving to a magnetic field strength of 7 T,

which provides increased SNR119 as well as the greater spectral separation, the experiment time

or voxel size could be further reduced.

The original motivation for development of DW-JPRESS in this thesis was to measure the

change in the diffusion properties of neurometabolites including lactate, associated with early

radiation treatment response. Upon developing a proof-of-principle DW-JPRESS sequence it

became apparent that this research application was premature given the suboptimal nature of

the prototype and the long experiment time. Alternatively, a separation of lactate and lipid

could be achieved through a biexponential model due to their substantially different diffusion

rates152. In addition, as lactate is one of the few metabolites that resides primarily in the

extracellular space it is unclear whether investigating the nature of its diffusion is beneficial.

DW-JPRESS does, however, provide the ability to measure other J-coupled resonances not

normally available at 3 T, which could be useful for a variety of other applications.

With the implementation of technical improvements, several interesting applications

could be explored with DW-JPRESS. It was shown in Chapter 4 that even with the proof-of-

principle version, the ADC of glutamate/glutamine was reliably quantified. This suggests that

DW-JPRESS could be combined with functional MRS (which combines functional magnetic

resonance-like tasks with MRS acquisition to investigate how the metabolite profiles change

with brain activation), to determine if glutamate changes with activation. It has previously been

observed that the ADCs of NAA, Cr and Cho increase by varying amounts with activation in the

visual cortex107. It would be interesting to test the hypothesis that glutamate, due to its role as

the main excitatory neurotransmitter46, exhibits increased diffusion during neuronal activation,

when it primarily resides in the synaptic cleft. Furthermore DW-JPRESS may be used to provide

information about microstructural changes in pathologies which are known to have a large

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disruption of normal metabolites, such as schizophrenia45, bi-polar, post-traumatic stress

disorder, obsessive-compulsive disorder and unipolar major depression172, as well as stroke173.

5.4 Early Radiation Treatment Response

It has previously been shown that brain cancer patients who had an increase in the ratio of

Cho/NAA at the 3rd week of treatment as compared to the start of treatment had a significantly

greater chance of early progression69. This ratio of choline and NAA is dependent on the

underlying concentration of the two metabolites, as well as the respective T1 and T2 values. It is

therefore hypothesized that by using spectroscopic methods to quantify T1, T2 and

concentration of choline and NAA it will provide improved ability to detect response to

radiation therapy compared to the use of metabolite ratios. A similar experiment to the one

which showed a correlation between Cho/NAA and treatment response69 should be undertaken

which measures the quantitative values using the procedure described in Chapter 3. In

addition, an optimized measurement of T2 values, as well as the ADCs of the metabolites, since

these will be susceptible to changes in tissue microstructure and could be a useful additional

biomarker of therapeutic response. The CSSMRS method (as developed in Chapter 2) with

BASING lactate editing (as used in Chapter 3) and the recommended TR of 2830 ms (based on

the T1 of lactate from Chapter 3) should be performed to measure the lipid-free lactate levels,

as well as to provide a control by measuring the contralateral normal-appearing brain tissue. In

addition to investigate the diffusion characteristics of metabolites in response to radiation an

optimized DW-JPRESS sequence should be used, as developed in Chapter 4 (provided the scan

duration can be reduced sufficiently). All the above measurements, including the clinical MRI

protocol need to be completed in less than one hour and ideally repeated weekly throughout

the radiation treatment.

Previously an increase in the Cho/NAA ratio after the 3rd week of radiation therapy

resulted in a hazard ratio of 2.7269. Using this as a lower bound for the hazard ratio (as we will

be at least as sensitive to metabolite changes as this previous work), a sample size of 31

patients should be used for this study using the proportional-hazards regression model174, with

a type I error rate of 0.05 and a type II error rate of 0.20. By making these quantitative

115

measurements it will be possible to investigate which values (or combination of values) provide

the best predictor for early radiation treatment response and the earliest time point at which

significant changes can be measured. Ultimately, this protocol could provide information useful

for clinicians in determining the optimal radiation treatment course for patients with brain

cancer. This could save non-responding patients unnecessary treatment, thereby improving

their quality of life in the short term and reducing the financial burden on our healthcare

system. Alternatively non-responders could also be selected for other treatment regimens such

as chemotherapy agents, with the hope of extending survival.

5.5 Final Remarks

Although the phenomenon of NMR has been known about for over 70 years (and much of the

underlying theoretical physics was developed by the early 1950s) the field continues to grow

and make profound impact in applications involving solid state physics, organic and physical

chemistry and, as discussed in this thesis, medicine. Over the past 30 years, in vivo MRS has

been developed and applied to a wide variety of pathologies in both the brain (as discussed

here) and other parts of the body. Continual improvements in MRS capabilities in terms of

hardware and technical developments as well as the demonstration of its benefits are allowing

MRS to become more applicable in a clinical setting. It will be interesting to see how MRS grows

in the coming decades.

116

Bibliography 1. Malom H. Levit Spin Dynamics. (2008).

2. Sørensen, O., Eich, G., Levitt, M., Bodenhausen, G. & Ernst, R. Product operator

formalism for the description of NMR pulse experiments. Progress in NMR Spectroscopy

16, 163–192 (1984).

3. Hahn, E. Spin echoes. Physical Review 80, 580–594 (1950).

4. Bottomley, P. A. Spatial Localization in NMR Spectroscopy in Vivo. Ann N Y Acad Sci 508,

333–348 (1987).

5. Haase, A., Frahm, J., Matthaei, D., Hanicke, W., Bomsdorf, H., Dietmar, K., Tischler, R. MR

imaging using stimulated echoes (STEAM). Radiology 160, 787–790 (1986).

6. McDonald, G. & Leigh, J. A new method for measuring longitudinal relaxation times.

Journal of Magnetic Resonance (1969) 362, 358–362 (1973).

7. Carr, H. & Purcell, E. Effects of diffusion on free precession in nuclear magnetic

resonance experiments. Physical review 94, 630–638 (1954).

8. Lee, H. K., Yarnan, A. & Nalcioglu, O. Homonuclear J-Refocused Spectral Editing

Technique for Quantification of Glutamine and Glutamate bv 1H NMR Spectroscopy.

Magn Reson Med 34, 253–259 (1995).

9. Star-Lack, J., Nelson, S. J., Kurhanewicz, J., Huang, L. R. & Vigneron, D. B. Improved water

and lipid suppression for 3D PRESS CSI using RF band selective inversion with gradient

dephasing (BASING). Magn Reson Med 38, 311–321 (1997).

10. Bax, A. & Lerner, L. Two-dimensional nuclear magnetic resonance spectroscopy. Science

232, 960–967 (1986).

117

11. Aue, W., Bartholdi, E. & Ernst, R. Two‐dimensional spectroscopy. Application to nuclear

magnetic resonance. The Journal of Chemical Physics 64, 2229–2246 (1976).

12. Macura, S. & Ernst, R. R. Elucidation of cross relaxation in liquids by two-dimensional

N.M.R. spectroscopy. Molecular Physics 41, 95–117 (1980).

13. Thomas, M. A., Hattori, N., Umeda, M., Sawada, T. & Naruse, S. Evaluation of two-

dimensional L-COSY and JPRESS using a 3 T MRI scanner: from phantoms to human brain

in vivo. NMR Biomed 16, 245–51 (2003).

14. Schulte, R. F. & Boesiger, P. ProFit: two-dimensional prior-knowledge fitting of J-resolved

spectra. NMR Biomed 19, 255–63 (2006).

15. Fuchs, A., Boesiger, P., Schulte, R. F. & Henning, A. ProFit revisited. Magn Reson Med 71,

458–68 (2014).

16. Schulte, R. F., Lange, T., Beck, J., Meier, D. & Boesiger, P. Improved two-dimensional J-

resolved spectroscopy. NMR Biomed 19, 264–70 (2006).

17. Prost, R. W. Magnetic resonance spectroscopy. Medical Physics 35, 4530 (2008).

18. Arnold, J. T., Dharmatti, S. S. & Packard, M. E. Chemical Effects on Nuclear Induction

Signals from Organic Compounds. The Journal of Chemical Physics 19, 507 (1951).

19. Haase, A., Frahm, J., Hänicke, W. & Matthaei, D. 1H NMR chemical shift selective (CHESS)

imaging. Phys Med Biol 30, 341–344 (1985).

20. Klose, U. In Vivo Proton Spectroscopy in Presence of Eddy Currents. Magn Reson Med 30,

26–30 (1990).

21. Jehenson, P., Westphal, M. & Schuff, N. Analytical Method for the Compensation of

Eddy-Current Effects Induced by Pulsed Magnetic Field Gradients in NMR Systems. Jour

Magn Reson 90, 264–278 (1990).

118

22. Kreis, R. Issues of spectral quality in clinical 1H-magnetic resonance spectroscopy and a

gallery of artifacts. NMR Biomed 17, 361–81 (2004).

23. Felblinger, J., Kreis, R. & Boesch, C. Effects of physiologic motion of the human brain

upon quantitative 1 H-MRS : analysis and correction by retro- gating†. NMR Biomed 11,

107–114 (1998).

24. Lange, T., Dydak, U., Roberts, T. P. L., Bjeljac, M. & Boesiger, P. Pitfalls in Lactate

Measurements at 3T. Am J Neuroradiol 27, 895–901 (2006).

25. Edden, R. a E., Schär, M., Hillis, A. E. & Barker, P. B. Optimized Detection of Lactate at

High Fields Using Inner Volume Saturation. Magn Reson Med 56, 912–917 (2006).

26. Starck, G., Carlsson, A., Ljungberg, M. & Forssell-Aronsson, E. k-space analysis of point-

resolved spectroscopy (PRESS) with regard to spurious echoes in in vivo (1)H MRS. NMR

Biomed 22, 137–47 (2009).

27. Govindaraju, V., Young, K. & Maudsley, A. A. Proton NMR chemical shifts and coupling

constants for brain metabolites. NMR Biomed 13, 129–153 (2000).

28. Coyle, J. T. The Nagging Question of the Function of N-Acetylaspartylglutamate.

Neurobiology of Disease 4, 231–238 (1997).

29. Brand, A., Richter-Landsberg, C. & Leibfritz, D. Multinuclear NMR Studies on the Energy

Metabolism of Glial and Neuronal Cells. Dev Neurosci 15, 289–298 (1993).

30. Haris, M., Cai, K., Singh, A., Hariharan, H. & Reddy, R. In vivo mapping of brain myo-

inositol. Neuroimage 54, 2079–2085 (2011).

31. Seaquist, E. & Gruetter, R. Identification of a High Concentration of Scyllo-‐inositol in the

Brain of a Healthy Human Subject Using 1H- and 13C-NMR. Magn Reson Med 39, 313–

316 (1998).

119

32. Ross, B. Biochemical Considerations in 1H Spectroscopy. Glutamate and Glutamine; Myo-

inositol and Related Metabolites. NMR Biomed 4, 59–63 (1991).

33. Kalra, S., Hanstock, C. C., Martin, W. R. W., Allen, P. S. & Johnston, W. S. Detection of

Cerebral Degeneration in Amyotrophic Lateral Sclerosis Using High-Field Magnetic

Resonance Spectroscopy. Arch Neurol 63, 1144–1148 (2006).

34. Saunders, D. E., Howe, F. A., Boogaart, A. v d, Griffiths, J. R. & Brown, M. M. Aging of the

Adult Human Brain: In Vivo Quantitation of Metabolite Content With Proton Magnetic

Resonance Spectroscopy. Jour Magn Reson Imag 9, 711–716 (1999).

35. Madan, A. et al. Proton T2 Measurement and Quantification of Lactate in Brain Tumors

by MRS at 3 Tesla In Vivo. Magn Reson Med 73, 2094–9 (2015).

36. Li, B. S. Y., Wang, H. & Gonen, O. Metabolite ratios to assumed stable creatine level may

confound the quantification of proton brain MR spectroscopy. Magn Reson Imaging 21,

923–928 (2003).

37. Dragon, F. et al. Behavioral Effects of L-a-Glycerylphosphorylcholine: Influence on

Cognitive Mechanisms in the Rat. Pharmacol Biochem Be 41, 445–448 (1992).

38. Millington, W. R. & Wurtman, R. J. Choline Administration Elevates Brain

Phosphorylcholine Concentrations. Jour Neurochem 6, 1748–1752 (1982).

39. Hasselmo, M. The Role of Acetylcholine in Learning and Memory. Curr Opin Neurobiol 16,

710–715 (2006).

40. Herholz, K. et al. In Vivo Imaging of Glucose Consumption and Lactate Concentration in

Human Gliomas. Ann Neurol 31, 319–327 (1991).

41. Lin, A.-Q., Shou, J.-X., Li, X.-Y., Ma, L. & Zhu, X.-H. Metabolic changes in acute cerebral

infarction: Findings from proton magnetic resonance spectroscopic imaging. Exp Ther

Med 7, 451–455 (2014).

120

42. Chu, W. et al. Psychiatry Research : Neuroimaging Magnetic resonance spectroscopy

imaging of lactate in patients with bipolar disorder. Psychiatry Res Neuroimaging 213,

230–234 (2013).

43. Horska A, Barker, P. B. Imaging of Brain Tumors: MR Spectroscopy and Metabolic

Imaging. Neuroimag Clin N Am 20, 293–310 (2010).

44. Graff-Radford, J. & Kantarci, K. Magnetic resonance spectroscopy in Alzheimer’s disease.

Neuropsychiatr Dis Treat 9, 687–696 (2013).

45. Port, J. D. & Agarwal, N. MR spectroscopy in schizophrenia. Jour Magn Reson Imag 34,

1251–61 (2011).

46. Petroff, O. A. C. GABA and Glutamate in the Human Brain. Neuroscientist 8, 562–573

(2002).

47. Bröer, S. et al. Alanine metabolism, transport, and cycling in the brain. J Neurochem 102,

1758–70 (2007).

48. Harrison, F. & May, J. Vitamin C Function in the Train: Vital Role of the Ascorbate

Transporter (SVCT2). Free Radic Biol Med 46, 719–730 (2009).

49. Storck, T. & Schulte, S. Structure, expression, and functional analysis of a Na(+)-

dependent glutamate/aspartate transporter from rat brain. Proc Natl Acad Sci USA 89,

10955–10959 (1992).

50. Albrecht, J., Sidoryk-Węgrzynowicz, M., Zielińska, M. & Aschner, M. Roles of glutamine in

neurotransmission. Neuron Glia Biology 6, 263–76 (2011).

51. Campistron, G., Buijs, R. & Geffard, M. Glycine neurons in the brain and spinal cord.

Antibody production and immunocytochemical localization. Brain Res 376, 400–405

(1986).

121

52. Dringen, R. Metabolism and functions of glutathione in brain. Prog Neurobiol 62, 649–

671 (2000).

53. Bakovic, M., Fullerton, M. D. & Michel, V. Metabolic and molecular aspects of

ethanolamine phospholipid biosynthesis: the role of CTP: phosphoethanolamine

cytidylyltransferase (Pcyt2). Biochem Cell Biol 300, 283–300 (2007).

54. Jia, F. et al. Taurine is a potent activator of extrasynaptic GABA(A) receptors in the

thalamus. J Neurosci 28, 106–15 (2008).

55. Porter, K. R., McCarthy, B. J., Freels, S., Kim, Y. & Davis, F. G. Prevalence estimates for

primary brain tumors in the United States by age, gender, behavior, and histology.

Neuro-Oncology 12, 520–7 (2010).

56. DeAngelis, L. Brain Tumors. N Engl J Med 344, 114–123 (2001).

57. Ricard, D. et al. Primary brain tumours in adults. Lancet 379, 1984–1996 (2012).

58. Gropler, R. et al. Comparison of Carbon-11-Acetate With Fluorine-18-Fluorodeoxyglucose

for Delineating Viable Myocardium by Positron Emission Tomography. JACC 22, 1587–

1597 (1993).

59. Smith, J. S. et al. Role of extent of resection in the long-term outcome of low-grade

hemispheric gliomas. Journal of clinical oncology : official journal of the American Society

of Clinical Oncology 26, 1338–45 (2008).

60. Terpstra, M. et al. Relationships Among Lactate Concentration, Blood Flow and

Histopathologic Profiles in Rat C6 Glioma. NMR Biomed 9, 185–194 (1996).

61. Kousi, E. et al. Spectroscopic Evaluation of Glioma Grading at 3T: The Combined Role of

Short and Long TE. Sci World J 2012, 1–11 (2012).

122

62. Wang, W. et al. Evaluation of the Diagnostic Performance of Magnetic Resonance

Spectroscopy in Brain Tumors: A Systematic Review and Meta-Analysis. PloS one 9,

e112577 (2014).

63. Wang, X. et al. Comparison of magnetic resonance spectroscopy and positron emission

tomography in detection of tumor recurrence in posttreatment of glioma: A diagnostic

meta-analysis. Asia Pac J Clin Oncol 11, 97–105 (2015).

64. Choi, C. et al. 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-

mutated patients with gliomas. Nat Med 18, 624–629 (2012).

65. Yan, H., Parsons, D. & Jin, G. IDH1 and IDH2 mutations in gliomas. Curr Neurol Neurosci

Rep 13, 345–357 (2009).

66. Theony, H. C. & Ross, B. D. Predicting and Monitoring Cancer Treatment Response with

DW-MRI. J Magn Reson Imaging 32, 2–16 (2010).

67. Al-Mefty, O., Kersh, J., Routh, A. & Smith, R. The long-term side effects of radiation

therapy for benign brain tumors in adults. Journal of neurosurgery 73, 502–512 (1990).

68. Sagiyama, K. et al. In vivo chemical exchange saturation transfer imaging allows early

detection of a therapeutic response in glioblastoma. PNAS 111, 4542–7 (2014).

69. Muruganandham, M. et al. 3D-MR Spectroscopic Imaging at 3Tesla for Early Response

Assessment of Glioblastoma Patients during External Beam Radiation Therapy. Int J

Radiat Oncol Biol Phys 90, 181–189 (2014).

70. Verma, N., Cowperthwaite, M. C., Burnett, M. G. & Markey, M. K. Differentiating tumor

recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies.

Neuro-oncology 15, 515–34 (2013).

71. Fink, K. R. & Fink, J. R. Imaging of brain metastases. Surg Neurol Int 4, S209–19 (2013).

123

72. John Pauly, Dwight Nishimura, A. M. A k-Space Analysis of Small-Tip-Angle Excitation.

Jour Magn Reson 81, 43–56 (1989).

73. Nishimura, D. G. Principles of Magnetic Resonance Imaging. (2010).

74. Shinnar, M., Bolinger, L. & Leigh, J. S. The Use of Finite Impulse Response Filters in Pulse

Design. Magnetic Resonance in Medicine 12, 81–87 (1989).

75. Pauly, J., Le Roux, P., Nishimura, D. & Macovski, A. Parameter Relations for the Shinnar-

Le Roux Selective Excitation Pulse Design Algorithm. IEEE Trans Med Imag 10, 53–65

(1991).

76. Silver, M. S., Joseph, R. I., Chen, C. N., Sank, V. J. & Hoult, D. I. Selective population

inversion in NMR. Nature 310, 681–683 (1984).

77. Silver, M. S., Joseph, R. I. & Hoult, D. I. Highly Selective pi/2 and pi Pulse Generation. Jour

Magn Reson 59, 347–351 (1984).

78. Hennig, J. Echoes—how to generate, recognize, use or avoid them in MR-imaging

sequences. Part I: Fundamental and not so fundamental properties of spin echoes.

COncept Magnetic Res 3, 125–143 (1991).

79. Academy, N. & States, U. NMR chemical shift imaging in three dimensions. Proc Natl

Acad Sci USA 79, 3523–3526 (1982).

80. Maudsley, A. A., Hilal, S. K., Perman, W. H. & Simon, H. E. Spatially Resolved High

Resolution Spectroscopy by “Four-Dimensional” NMR. Jour Magn Reson 51, 147–152

(1983).

81. Frahm, J. et al. Localized Proton NMR Spectroscopy in Different Regions of the Human

Brain in Viva Relaxation Times and Concentrations of Cerebral Metabolites. Magn Reson

Med 63, 47–63 (1989).

124

82. Spielman, D., Pauly, J., Macovski, A. & Enzmann, D. Spectroscopic Imaging with

Multidimensional Pulses for Excitation: SIMPLE. Magn Reson Med 19, 67–84 (1991).

83. Segebarth, C. M., Balériaux, D. F., Luyten, P. R. & Den Hollander, J. A. Detection of

Metabolic Heterogeneity of Human Intracranial Tumors in Vivo by 1H NMR Spectroscopic

Imaging. Magn Reson Med 13, 62–76 (1990).

84. Roemer, P. B., Edelstein, W. a, Hayes, C. E., Souza, S. P. & Mueller, O. M. The NMR

Phased Array. Magn Reson Med 16, 192–225 (1990).

85. Griswold, M. a et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA).

Magn Reson Med 47, 1202–10 (2002).

86. Pruessmann, K. P., Weiger, M., Scheidegger, M. B. & Boesiger, P. SENSE: Sensitivity

Encoding for Fast MRI. Magn Reson Med 42, 952–62 (1999).

87. Lin, F.-H. et al. Sensitivity-encoded (SENSE) proton echo-planar spectroscopic imaging

(PEPSI) in the human brain. Magnetic Resonance in Medicine 57, 249–57 (2007).

88. Bonekamp, D., Smith, M. a, Zhu, H. & Barker, P. B. Quantitative SENSE-MRSI of the

human brain. Magn Reson Imaging 28, 305–13 (2010).

89. Kuo, A. Y.-C., Chiew, M., Tam, F., Cunningham, C. & Graham, S. J. Multiecho Coarse Voxel

Acquisition for Neurofeedback fMRI. Magn Reson Med 65, 715–24 (2011).

90. Chiew, M. & Graham, S. J. Constrained Source Space Imaging: Application to Fast, Region-

Based Functional MRI. Magn Reson Med 70, 1058–1069 (2012).

91. Provencher, S. Estimation of Metabolite Concentrations from Localized in Vivo Proton

NMR spectra. Magn Reson Med 30, 672–679 (1993).

92. Naressi, A., Couturier, C., Castang, I., De Beer, R. & Graveron-Demilly, D. Java-based

graphical user interface for MRUI, a software package for quantitation of in vivo/medical

magnetic resonance spectroscopy signals. Comput Biol Med 31, 269–86 (2001).

125

93. Poullet, J. & Sima, D. An automated quantitation of short echo time MRS spectra in an

open source software environment: AQSES. NMR Biomed 20, 493–504 (2007).

94. Reynolds, G., Wilson, M., Peet, A. & Arvanitis, T. N. An Algorithm for the Automated

Quantitation of Metabolites in in Vitro NMR Signals. Magn Reson Med 56, 1211–1219

(2006).

95. Longo, R., Pollesello, P. & Ricci, C. Proton MR Spectroscopy in Quantitative in Vivo

Determination of Fat Content in Human liver Steatosis. Jour Magn Reson Imag 5, 281–

285 (1995).

96. Li, C., Kuo, Y., Chen, CY Kuo, YT, Chiu, YY She, F. & Liu, G. Quantification of choline

compounds in human hepatic tumors by proton MR spectroscopy at 3 T. Magn Reson

Med 776, 770–776 (2005).

97. Woessner, D. E. Effects of Diffusion in Nuclear Magnetic Resonance Spin-Echo

Experiments. J Chem Phys 34, 2057 (1961).

98. Stejskal, E. O. & Tanner, J. E. Spin Diffusion Measurements: Spin Echoes in the Presence

of a Time-Dependent Field Gradient. J Chem Phys 42, 288 (1965).

99. Bihan, D. Molecular Diffusion, Tissue Microdynamics and Microstructure. NMR Biomed 8,

375–386 (1995).

100. Ercan, A. E., Techawiboonwong, A., Versluis, M. J., Webb, A. G. & Ronen, I. Diffusion-

weighted chemical shift imaging of human brain metabolites at 7T. Magn Reson Med 73,

2053–2061 (2015).

101. Posse, S., Cuenod, C. & Bihan, D. Le Human Brain: Proton Diffusion MR Spectroscopy.

Radiology 188, 719–725 (1993).

102. Ellegood, J., Hanstock, C. C. & Beaulieu, C. Trace apparent diffusion coefficients of

metabolites in human brain using diffusion weighted magnetic resonance spectroscopy.

Magn Reson Med 53, 1025–32 (2005).

126

103. Najac, C., Branzoli, F., Ronen, I. & Valette, J. Brain intracellular metabolites are freely

diffusing along cell fibers in grey and white matter, as measured by diffusion-weighted

MR spectroscopy in the human brain at 7 T. Brain Struct Funct 221, 1245–1254 (2014).

104. Ronen, I., Ercan, E. & Webb, A. Axonal and glial microstructural information obtained

with diffusion-weighted magnetic resonance spectroscopy at 7T. Front Integr Neurosci 7,

1–9 (2013).

105. Wood, E. T. et al. Reproducibility and optimization of in vivo human diffusion-weighted

MRS of the corpus callosum at 3 T and 7 T. NMR Biomed 28, 976–87 (2015).

106. Ercan, E. et al. Glial and axonal changes in systemic lupus erythematosus measured with

diffusion of intracellular metabolites. Brain 1–11 (2016).

107. Branzoli, F., Techawiboonwong, A., Kan, H., Webb, A. & Ronen, I. Functional diffusion-

weighted magnetic resonance spectroscopy of the human primary visual cortex at 7 T.

Magn Reson Med 69, 303–309 (2013).

108. Kan, H. E. et al. Differences in Apparent Diffusion Coefficients of Brain Metabolites

Between Grey and White Matter in the Human Brain Measured at 7 T. Magn Reson Med

67, 1203–1209 (2012).

109. Cao, P. & Wu, E. X. In vivo diffusion MRS investigation of non-water molecules in

biological tissues. NMR Biomed [epub] (2016).

110. Gu, M., Liu, C. & Spielman, D. M. Parallel Spectroscopic Imaging Reconstruction with

Arbitrary Trajectories Using k-space Sparse Matrices. Magn Reson Med 61, 267–72

(2009).

111. Posse, S., Otazo, R., Tsai, S.-Y., Yoshimoto, A. E. & Lin, F.-H. Single-shot magnetic

resonance spectroscopic imaging with partial parallel imaging. Magn Reson Med 61,

541–547 (2009).

127

112. Oshio, K., Kyriakos, W. & Mulkern, R. V Line Scan Echo Planar Spectroscopic Imaging.

Magn Reson Med 44, 521–524 (2000).

113. Frahm, J., Merboldt, K.-D. & Hänicke, W. Localized Proton Spectroscopy using Stimulated

Echoes. Jour Magn Reson 72, 502–508 (1987).

114. Inglese, M. et al. Field, Coil, and Echo-Time Influence on Sensitivity and Reproducibility of

Brain Proton MR Spectroscopy. Am J Neuroradiol 27, 684–8 (2006).

115. Jin, J., Liu, F., Weber, E., Li, Y. & Crozier, S. An electromagnetic reverse method of coil

sensitivity mapping for parallel MRI–Theoretical framework. Jour Magn Reson 207, 59–

68 (2010).

116. M. J. Allison, S. Ramani, J. A. F. Regularized MR Coil Sensitivity Estimation Using

Augmented Lagrangian Methods. International Symposium on Biomedical Imaging (ISBI)

32, 394–397 (2012).

117. Allison, M. J., Ramani, S. & Fessler, J. a Accelerated Regularized Estimation of MR coil

Sensitivities Using Augmented Lagrangian Methods. IEEE Trans Med Imaging 32, 556–64

(2013).

118. Chen, L., Weng, Z., Goh, L. & Garland, M. An efficient algorithm for automatic phase

correction of NMR spectra based on entropy minimization. Jour Magn Reson 158, 164–

168 (2002).

119. Li, Y. et al. T1 and T2 Metabolite Relaxation Times in Normal Brain at 3T and 7T. J Mol

Imag Dynamic 1–5 (2013).

120. Ryner, L., Sorenson, J. & Thomas, M. Localized 2D J-resolved 1 H MR spectroscopy:

Strong coupling effects in vitro and in vivo. Magn Reson Imaging 13, 853–869 (1995).

121. Gill SS, Thomas DG, Van Bruggen N, Gadian DG, Peden CJ, Bell JD, Cox IJ, Menon DK, Iles

RA, Bryant DJ, et al. Proton MR Spectroscopy of Intracranial Tumours: In Vivo and In Vitro

Studies. J Comput Assist Tomogr 14, 497–504 (1990).

128

122. Li, Y. et al. Comparison of T1 and T2 metabolite relaxation times in glioma and normal

brain at 3T. J Magn Reson Imaging 28, 342–350 (2008).

123. Sijens, P. E. & Oudkerk, M. 1H chemical shift imaging characterization of human brain

tumor and edema. Eur Radiol 12, 2056–61 (2002).

124. Ethofer, T. et al. Comparison of Longitudinal Metabolite Relaxation Times in Different

Regions of the Human Brain at 1.5 and 3 Tesla. Magn Reson Imaging 50, 1296–1301

(2003).

125. Mylnarik, V., Gruber, S. & Moser, E. Proton T1 and T2 relaxation times of human brain

metabolites at 3 Tesla. NMR Biomed 14, 325–331 (2001).

126. Look, D. C. & Locker, D. R. Time Saving in Measurement of NMR and EPR Relaxation

Times. Rev Sci Instrum 41, 250–251 (1970).

127. Gupta, R. K., Ferretti, J. A., Becker, E. D. & Weiss, G. H. A Modified Fast Inversion-

Recovery Technique for Spin-Lattice Relaxation Measurements *. Jour Magn Reson 38,

447–452 (1980).

128. Star-Lack, J. et al. In Vivo Lactate Editing with Simultaneous Detection of Choline,

Creatine, NAA, and Lipid Singlets at 1.5 T Using PRESS Excitation with Applications to the

Study of Brain and Head and Neck Tumors. J Magn Reson 133, 243–254 (1998).

129. Kelley, D. A. C., Wald, L. L. & Star-lack, J. M. Lactate Detection at 3T : Compensating J

Coupling Effects With BASING. J Magn Reson Imaging 9, 732–737 (1999).

130. Ogg, R. J. & Kingsley, P. B. Optimized Precision of Inversion-Recovery T1 Measurements

for Constrained Scan Time. Magn Reson Med 51, 625–30 (2004).

131. Chen, A. P. et al. T1 measurement of brain metabolites at 3T with a saturated-inversion

recovery method. Proc Intl Soc Mag Reson Med 15, 1376 (2007).

129

132. Park, I. et al. Implementation of 3 T Lactate-Edited 3D 1H MR Spectroscopic Imaging with

Flyback Echo-Planar Readout for Gliomas Patients. Ann Biomed Eng 39, 193–204 (2011).

133. Schirmer, T. & Auer, D. P. On the reliability of quantitative clinical magnetic resonance

spectroscopy of the human brain. NMR Biomed 13, 28–36 (2000).

134. Träber, F., Block, W., Lamerichs, R., Gieseke, J. & Schild, H. H. 1H Metabolite Relaxation

Times at 3.0 Tesla: Measurements of T1 and T2 Values in Normal Brain and

Determination of Regional Differences in Transverse Relaxation. J Magn Reson Imaging

19, 537–45 (2004).

135. Minati, L., Aquino, D., Bruzzone, M. & Erbetta, A. Quantitation of normal metabolite

concentrations in six brain regions by in-vivo 1 H-MR spectroscopy. J Med Phys 35, 154–

163 (2010).

136. Howe, F. A. et al. Metabolic Profiles of Human Brain Tumors Using Quantitative In Vivo

1H Magnetic Resonance Spectroscopy. Magn Reson Med 49, 223–232 (2003).

137. Bolinger, L. & Leigh, J. S. Hadamard Spectroscopic Imaging (HSI) for Multivolume

Localization. J Magn Reson 80, 162–167 (1988).

138. Landheer, K., Sahgal, A., Das, S. & Graham, S. J. Constrained Source Space MR

Spectroscopy: Multiple Voxels, No Gradient Readout. Am J Neuroradiol 37, 1–8 (2015).

139. Boer, V. O., Klomp, D. W. J., Laterra, J. & Barker, P. B. Parallel Reconstruction in

Accelerated Multivoxel MR Spectroscopy. Magn Reson Med 74, 599–606 (2015).

140. Payne, G. S., DeSouza, N. M., Messiou, C. & Leach, M. O. Single-shot single-voxel lactate

measurements using FOCI-LASER and a multiple-quantum filter. NMR Biomed 28, 496–

504 (2015).

141. Near, J., Simpson, R., Cowen, P. & Jezzard, P. Efficient γ-aminobutyric acid editing at 3T

without macromolecule contamination: MEGA-SPECIAL. NMR Biomed 24, 1277–1285

(2011).

130

142. Andreychenko, A., Boer, V. O., Arteaga de Castro, C. S., Luijten, P. R. & Klomp, D. W. J.

Efficient Spectral Editing at 7 T: GABA Detection with MEGA-sLASER. Magn Reson Med

68, 1018–1025 (2012).

143. Nicolay, K., Braun, K. P. J., Graaf, R. A. De, Dijkhuizen, R. M. & Kruiskamp, M. J. Diffusion

NMR spectroscopy. NMR Biomed 14, 94–111 (2001).

144. Harada, M. et al. Diffusion-weighted in vivo localized proton MR spectroscopy of human

cerebral ischemia and tumor. NMR Biomed 15, 69–74 (2002).

145. Ellegood, J., Hanstock, C. C. & Beaulieu, C. Diffusion Tensor Spectroscopy of Myo-Inositol

in Human Brain. Proc Intl Soc Mag Reson Med 16, 573 (2008).

146. Dreher, W., Busch, E. & Leibfritz, D. Changes in Apparent Diffusion Coefficients of

Metabolites in Rat Brain After Middle Cerebral Artery Occlusion Measured by Proton

Magnetic Resonance Spectroscopy. Magn Reson Med 389, 383–389 (2001).

147. Pfeuffer, J., Tkáč, I. & Gruetter, R. Extracellular–intracellular distribution of glucose and

lactate in the rat brain assessed noninvasively by diffusion-weighted 1H nuclear magnetic

resonance. J Cereb Blood Flow Metab 20, 736–746 (2000).

148. Mescher, M., Merkle, H., Kirsch, J., Garwood, M. & Gruetter, R. Simultaneous in vivo

spectral editing and water suppression. NMR Biomed 272, 266–272 (1998).

149. Sotak, C. H. A Method for Measuring the Apparent Self-Diffusion Coefficient of in Vivo

Lactic Acid Using Double-Quantum Coherence-Transfer Spectroscopy. Jour Magn Reson

90, 198–204 (1990).

150. Sotak, C. & Moore, S. A volume-localized, two-dimensional NMR method for lactate-

edited diffusion coefficient measurements using zero-quantum coherence-transfer. Jour

Magn Reson 92, 581–589 (1991).

131

151. Trimble, L., Shen, J., Wilman, A. & Allen, P. Lactate editing by means of selective-pulse

filtering of both zero-and double-quantum coherence signals. Jour Magn Reson 86, 191–

198 (1990).

152. Wang, A. M. et al. Separation and quantification of lactate and lipid at 1.3 ppm by

diffusion-weighted magnetic resonance spectroscopy. Magn Reson Med 77, 480–489

(2017).

153. Yue, K., Marumoto, A., Binesh, N. & Thomas, M. A. 2D JPRESS of human prostates using

an endorectal receiver coil. Magn Reson Med 47, 1059–64 (2002).

154. Posse, S., DeCarli, C. & Bihan, D. Le Three-dimensional echo-planar MR spectroscopic

imaging at short echo times in the human brain. Radiology 192, 733–738 (1994).

155. Smith, S., Levante, T., Meier, B. & Ernst, R. Computer simulations in magnetic resonance.

An object-oriented programming approach. J Magn Reson Ser A 75–105 (1994).

156. Cuenod, C., Posses, S., Mattiello, J. & Le Bihan, D. Relationship between Diffusion and

Temperature for N-Acetyl Aspartate and Choline : A Localized 1H NMR Spectroscopy

study. Proc. SMRM 11th Annual Meeting, Berlin 1215 (1992).

157. Holz, M., Heila, S. R. & Saccob, A. Temperature-dependent self-diffusion coefficients of

water and six selected molecular liquids for calibration in accurate 1H NMR PFG

measurements. Phys Chem Chem Phys 2, 4740–4742 (2000).

158. Ligneul, C., Palombo, M. & Valette, J. Metabolite diffusion up to very high b in the mouse

brain in vivo: Revisiting the potential correlation between relaxation and diffusion

properties. Magn Reson Med [Epub ahead of print] (2016).

159. Branzoli, F., Ercan, E., Webb, A. & Ronen, I. The interaction between apparent diffusion

coefficients and transverse relaxation rates of human brain metabolites and water

studied by diffusion-weighted spectroscopy at 7 T. NMR in biomedicine 27, 495–506

(2014).

132

160. Merboldt, K., Horstermann, D., Hanicke, W., Bruhn, H. & Frahm, J. Molecular self‐

diffusion of intracellular metabolites in rat brain in vivo Investigated by Localized Proton

NMR Diffusion Spectroscopy. Magn Reson Med 125–129 (1993).

161. Wick, M., Nagatomo, Y., Prielmeier, F. & Frahm, J. Alteration of Intracellular Metabolite

Diffusion in Rat Brain In Vivo During Ischemia and Reperfusion. Stroke 26, 1930–1934

(1995).

162. Toorn, A. Van Der, Dijkhuizen, R. M., Tulleken, C. A. F. & Nicolay, K. Diffusion of

Metabolites in Normal and Ischemic Rat Brain Measured by Localized 1H MRS. Magn

Reson Med 36, 914–922 (1996).

163. Abe, O. et al. Temporal Changes of the Apparent Diffusion Coefficients of Water and

Metabolites in Rats With Hemispheric Infarction: Experimental Study of

Transhemispheric Diaschisis in the Contralateral Hemisphere at 7 Tesla. J Cereb Blood

Flow Metab 20, 726–735 (2000).

164. Valette, J. et al. Diffusion-weighted NMR Spectroscopy Allows Probing of 13C Labeling of

Glutamate Inside Distinct Metabolic Compartments in the Brain. Magn Reson Med 60,

306–11 (2008).

165. Valette, J. et al. Isoflurane strongly affects the diffusion of intracellular metabolites, as

shown by 1H nuclear magnetic resonance spectroscopy of the monkey brain. J Cereb

Blood Flow Metab 27, 588–596 (2007).

166. Edden, R. A. E., Intrapiromkul, J., Zhu, H., Cheng, Y. & Barker, P. B. Measuring T2 In Vivo

With J-Difference Editing: Application to GABA at 3 Tesla. Jour Magn Reson Imag 35,

229–234 (2012).

167. Ellegood, J., Hanstock, C. C. & Beaulieu, C. Considerations for measuring the fractional

anisotropy of metabolites with diffusion tensor spectroscopy. NMR Biomed 24, 270–80

(2011).

133

168. Zhu, Y. Parallel Excitation With an Array of Transmit Coils. Magn Reson Med 51, 775–84

(2004).

169. Katscher, U., Börnert, P., Leussler, C. & Van den Brink, J. S. Transmit SENSE. Magn Reson

Med 49, 144–50 (2003).

170. Cavassila, S., Deval, S., Huegen, C., Ormondt, D. v & Graveron-Demilly, D. Cramér–Rao

bounds: an evaluation tool for quantitation. NMR Biomed 14, 278–283 (2001).

171. Bolliger, C. S., Boesch, C. & Kreis, R. On the use of Cramér-Rao minimum variance bounds

for the design of magnetic resonance spectroscopy experiments. NeuroImage 83, 1031–

40 (2013).

172. Maddock, R. & Buonocore, M. MR Spectroscopic Studies of the Brain in Psychiatric

Disorders. Curr Topics Behav Neurosci 11, 199–251 (2012).

173. Dani, K., An, L., Henning, E., Shen, J. & Warach, S. Multivoxel MR spectroscopy in acute

ischemic stroke comparison to the stroke protocol MRI. Stroke 43, 2962–2967 (2012).

174. Schoenfeld, D. Sample-size formula for the proportional-hazards regression model.

Biometrics 39, 499–503 (1983).

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