new developments in magnetic resonance spectrocopy and

Els FieremansSteven Delputte Mahir Ozdemir

New developments in MagneticResonance Spectrocopy and DiffusionMRI

[email protected] – fMRI symposium – 24/01/2006

OverviewMagnetic Resonance Spectroscopy (MRS)‣ Basic physics of MRS‣ Quantitative MRS ‣ Pitfalls‣ MRS of the prostate

Diffusion MRI‣ Basic physics of diffusion MRI‣ Sequence development‣ Validation (hardware phantom)‣ Diffusion Tensor Tractography ‣ Validation (software phantom)

More MR research at UGent

MR SpectroscopyDTI: acquisition & validationDTI: tractography & validation


General Introduction: Nuclear spin

‣A nucleus with an odd atomic number or an odd mass number has a nuclear spin.‣ The spinning charged nucleus generates a magnetic field.



General Introduction: Net magnetization M

‣ The magnetic fields of the spinning nuclei will align either parallel with the external field, or antiparallel to the field.

M



General Introduction: Larmor frequency

‣ ν0 is the Larmor precessionfrequency‣ γ is the gyromagnetic ratio(42.58 MHz/T for hydrogen)‣ B0 is the main magnetic field(typically 1T to 3T)

00 Bγν =ν0



General Introduction: RF excitation & FIDM is tilted from itsoriginal longitudinal z-axis orientation by B1 matching the larmor frequency of M.

The oscillation of Mxyproduces a fluctuating magnetic field that generates a current in the receiver coil: FID.

Spectrum: peak area is proportionalto proton concentration

ℑ



Introduction MRS: real spectrum

‣ If all the proton nuclei in a mixture of molecules had the same Larmor frequency, spectra would be limited to a single peak!

Mahir


IntroductionAbsolute quantificationPitfallsApplication


Introduction MRS: Magnetic shielding

↑ B0

a bare nucleus (H+)feels the full effect ofthe external field (B0)

electron density partiallyshields the nucleus from

B0 so it “feels” Blocal

electrons generate aninduced field (Bi)which opposes B0

↓ Bi

↑Blocal

locallocal B γν =

Mahir




Introduction MRS: chemical shift

‣ The difference between the resonance frequency and a standard reference frequency in Hz (chemical shift) is characteristic for each metabolite and is dependent on the magnetic field strength.‣ This difference, divided by that standard frequency is independent of the field strength:

δ (ppm)= shift (Hz) / frequency of excitation pulse (MHz)

Mahir




Introduction MRS: CSI versus SVS

Courtesy: Siemens

Chemical Shift ImagingSingle VoxelSpectroscopy

Mahir




Introduction MRS: Ratio based results

‣ Can generate mapsof certain metabolites‣ Maps of metaboliteratios such asNAA/Cre, or Cho/Cre

‣ The ratio based results can be used for the classification of tissues (eg. for discrimination between malignant versus benign tissues)

Ambiguity: is numerator or denominator changing?

Courtesy: GE

Mahir




Absolute quantification

‣ Resolves ambiguities caused by ratio based results.

‣ Correction for metabolite dependent values of T1, T2 and # of protons per molecule needed!!

‣ Choice of reference substance is of key importance:internal (creatine, water,…) versus external reference‣ Since last couple of years: internal water signal most popular as reference.

» Pathology related changes are relatively small compared to Cre» Concentration is very well known

ref

M

SS

xreferenceM ][][ =

Mahir




Absolute quantification

‣ Concentration of water:

‣ Concentration of metabolite is typically only in the order of 10mM!‣ Severe dynamic range problem (factor of 10000)!‣ But it is too time consuming to record both water unsuppressed and water suppressed data sets.

[ ]protonsMOmoleHHmoles

Mg

moleliter

g

=⇒

=×

1112

5.5518

11

1000

2

1

Mahir




Absolute quantification: Singular Value Decomposition

Unsuppressed waterspectrum SVD Metabolite spectrum

Mahir

107 104




Pitfall 1: Signal loss due to SVD

n (number of steps) Remaining signal loss (%)

5 11.7

20 3.9

50 1.3

100 0.98

Forward Problem

SVDP_true P_res

P_true>P_res

Inverse problem

Iterative

Algorithm

P_res P_trueIterativeAlgorithm

Mahir

18% signalloss




Pitfall 2: Sideband artifacts

Sitebands = gradient induced frequency modulations of the unsuppressed water signal

NAA

Residual water signal

Mahir




Pitfall 2: Corrected sideband artifacts

Mahir




Application: MRS of the prostate

Prostrate:2x4x3 cm, 20 g

Second cause of cancer related death in men (*)

*Imperial cancer research Fund, American Cancer Society Tumor

Reduced signal ratio between citrate & choline

Normal tissue

Mahir




Application: MRS of the prostate, validation with a pelvis phantom

ER coil

90 mM Citrate Solution

The phantom

m = 84.74 mM , std = 17.3 mMMahir




‣ DTI can disclose the 3D organization of fibrous tissue‣ DTT enables us to reconstruct non-invasively the white matter axonal pathways

General introduction to Diffusion MRI

MR SpectroscopyDTI: acquisition & validationDTI: tractography & validation Introduction


Basics of diffusion MRI

‣ The random movement of protons.

‣

Einstein equationD = diffusion coefficient

in free medium t = observation time

» Typically: 8μm in 35ms (D=1.0x10-3mm2s-1)

Dt2=step Mean



Origin of diffusion signal in brain white matter

Myelin Sheath

Axon terminals

DendritesCell bodyNucleusAxon

10 μm



IntroductionMR SpectroscopyDTI: acquisition & validationDTI: tractography & validation

Extra cellularFAST diffusion

Intra cellular SLOW diffusion

ExchangeIC / EC

Diffusion signal:


t

x

x

x

Bx

Bx

Δ



Basics of diffusion MRI‣By applying diffusion gradients, the random movement of protons in the extra cellular space along a chosen direction is measured (DWI).‣Molecular mobility is not the same in all directions due to barriers (myelin and axon membranes) → anisotropy‣DTI: probing the three-dimensional architecture of brain white matter‣Diffusion Tensor Tractography (DTT): non-invasive tool for reconstructing thewhite matter axonal pathways of the human brain in vivo.



Diffusion Tensor Imaging Sequences

‣ DTI in brain white matter: » Intra-voxel heterogeneity → a voxel may contain multiple fiber directions (eg. crossing fibers). » Low SNR

‣ Solution: Increase the number of acquisitions and angular resolution by applying diffusion gradients in many directions: 12-60 for DTI and 99-500 for High Angular Resolution Diffusion Imaging (HARDI, does not suppose any model for the diffusion).

‣ Speed of the sequencebecomes crucial!

Els


Spiral acquisitionValidation: head phantom


MRI Imaging

Basic principle of magnetic resonance imaging: k-space formalism

ℑ)(kSr

==)(rI r

Image space Frequence space

Fourier space

k-space

Els




K-space sampling strategies‣ Need for fast MR imaging sequences for fMRI, DTI, HARDI,…‣ 2 strategies for sampling the k-space rapidly:

Echo planar imaging (EPI) Spiral imaging

‣ Spiral sequences show some advantages/differences in comparison with cartesian EPI:

» Smoother trajectory → less demands on hardware performance» Less sensitive to motion artifacts.» Spirals (radial symmetric PSF) → blurring. » Cartesian EPI (anisotropic PSF)→ distortion artifacts

Els




K-space sampling strategies

Comparison between Cartesian EPI and Spiral Imaging

Els




DTI optimization strategies

‣Elimination of the artifacts:

Spiral image Spiral image

SHIMMING

Spiral image

Correction for eddy currents imperfection of the magnetic gradients



Els


Validation of DTI sequences

‣ In vivo

‣Hardware diffusion phantom

single shot spiral scan images with diffusion encoding along the x-, y-, z-direction and corresponding isotropic diffusion-weighted imaging (from left to right). Bammer R, Basic principles of diffusion-weighted imaging, European journal of radiology, 45: 169-184, 2003

Els




Validation: head diffusion phantom

‣Synthetic fibers to imitate the neural fascicle bundles.‣Anthropomorphic phantom of the major neural fiber tracts.‣MRI-compatibility: T1 and T2-relaxation times similar with brain white matter.‣DTI-compatibility: similar diffusion behavior as brain white matter (Monte Carlo diffusion simulations and quantitative measurements of DApp(t)-curves for different fiber materials).

Els




Validation: a phantom bundle

• 400 parallel wires tightly held together by a shrinking tube• Wire = woven strand of Ultrahigh-Molecular Weight Polyethylene fibers (UHMWPE) (Dyneema®)

• FA = 0.45 (± σ = 0.15)

Els







Els



FA FA

Fractional Anisotropy3T, TE = 60ms, TR= 3sspin echo sequence with TRSE-diffusion preparation 12 directions, b-factors of 0 and 700 s/mm²

Tracking result of the corticospinal tract.

FA = 0.321 (± σ = 0.15)

Els




DTT: reconstruction of axonal connections line propagation

Solve:

For each step, 2 decisions to make:‣ New direction?

» Principal diffusion direction» Tensor deflection» Tensor deflection with subpixel adaptive step size,…

‣ Integration method? » Euler (first order integration: )» Runge-Kutta (fourth order integration)» FACT (1999, Mori et al.),…

( ) eds

srd=

ecrr ii ⋅+=+1

Steven


DTT: introductionDRFTValidation: software phantom




DTT algorithms & visualization

Point to point rigid connections‣ Diagnostically valuable‣ Fast‣ Cumulative error propagation (spurious tracts)

“Likelihood of connectivity”maps

eg. Fast marching‣ More information‣ slower

Seedvoxel

Steven


Density Regularized Fiber Tracking (DRFT)Point to point connections+ pointwise estimate of probability + environmental architectural information

Based on the fact that the architectural environment plays a dominant role in the reproducibility of each tracking result

Steven




Density Regularized Fiber Tracking (DRFT)

Temporary trackCM tract

di dd σ7.1+>

Stop temporarytrack



Steven


DRFT results: visualization

Body of the corpus callosum

‣Color encodes directional information (A/P: green, I/S: blue, L/R: red)‣Transparency encodes estimate of probability‣Width encodes σd(dispersion)



Steven


DTT validation: framework

‣ In vivo DWI acquisition‣ Anisotropic smoothing of DWIs‣ RESTORE (robust tensor estimation)‣ DRFT ground truth fibers

‣ Build the anatomically realistic phantom1 (using environmental architectural information)

‣ Add noise & try to reconstruct the ground truth fibers‣ Compute similarity measures

1extension of work by A. Leemans (MRM 2005, 53: 944-953)

A

B

C




Phantom (b,d)

In vivo (a,c)

A good

between the colour codedsynthetic FA

images and the original in vivo

ones

correspondence is found

(a)

(b)

(c)

(d)

Steven




Validation results: similarity measurements

Steven




DRFT and DTT validation

‣ DRFT results in diagnostically valuable 3D pathways AND at the same time gives an estimate of probability.‣ By using in vivo DRFT results, we were able to build a noise-free and anatomically realistic dataset.

» Noise and MRI acquisition artifacts can be incorporated in the synthetic phantom as well.

‣ With an anatomically realistic synthetic DT dataset we can:

» quantitatively predict how a (new) DTT algorithm will perform on real in vivo data (and not just for ad hoc cases such as helices etc.). » optimize internal and operator dependant tractography parameters.

Steven




More MR research at Ugent (1.5T and 3T)‣ GifMi:

» fMRI studies of language and memory of epileptic patients, fMRI of stuttering» MRI techniques for measuring the biological malfunctioning of neurovascular units in migraine patients» Neuropsychology:

• Emotional disorders, mental rotation,…• cognitive dysfunctions ↔ structural brain damage in MS

patients

‣ Radiotherapy: » Quantitative T2-mapping for 3D geldosimetry & tissue classification» Molecular imaging

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Els FieremansSteven Delputte Mahir Ozdemir

New developments in MagneticResonance Spectrocopy and DiffusionMRI

new developments in magnetic resonance spectrocopy and

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