fmri data quality assurance and preprocessing jody culham department of psychology university of...
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fMRI Data Quality Assuranceand Preprocessing
Jody CulhamDepartment of Psychology
University of Western Ontario
Last Update: November 29, 2008fMRI Course, Louvain, Belgium
http://www.fmri4newbies.com/
The Black Box
• The danger of automated processing and fancy images is that you can get blobs without every really looking at the real data
• The more steps done at once, the greater the chance of problems
RawData
Big Black Box of automated
software
Pretty pictures
Know Thy Data• Look at raw functional images
– Where are the artifacts and distortions?
– How well do the functionals and anatomicals correspond
• Look at the movies– Is there any evidence of head motion?
– Is there any evidence of scanner artifacts (e.g., spikes)
• Look at the time courses– Is there anything unexpected (e.g., abrupt signal changes at the start of
the run)?
– What do the time courses look like in the unactivatable areas (ventricles, white matter, outside head)?
• Look at individual subjects• Double check effects of various transformations
– Make sure left and right didn’t get reversed
– Make sure functionals line up well with anatomicals following all transformations
• Think as you go. Investigate suspicious patterns.
Sample ArtifactsGhosts
Spikes
Metallic Objects (e.g., hair tie)Hardware Malfunctions
Calculating Signal:Noise Ratio
Pick a region of interest (ROI) outside the brain free from artifacts (no ghosts, susceptibility artifacts). Find mean () and standard deviation (SD).
Pick an ROI inside the brain in the area you care about. Find and SD.
SNR = brain/ outside = 200/4 = 50
[Alternatively SNR = brain/ SDoutside = 200/2.1 = 95(should be 1/1.91 of above because /SD ~ 1.91)]
Head coil should have SNR > 50:1
Surface coil should have SNR > 100:1
When citing SNR, state which denominator you used.
Source: Joe Gati, personal communication
e.g., =4, SD=2.1
e.g., = 200
WARNING!: computation of SNR is complicated for phased array coils
WARNING!: some software might recalibrate intensities so it’s best to do computations on raw data
Why SNR MattersNote: This SNR level is not based on the formula given
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
Sources of Noise
Physical noise• “Blame the magnet, the physicist, or the laws of physics”
Physiological noise• “Blame the subject”
A Map of Noise
• voxels with high variability shown in white
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
Field Strength
• Although raw SNR goes up with field strength, so does thermal and physiological noise
• Thus there are diminishing returns for increases in field strength
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
Coils
Head coil• homogenous signal• moderate SNR
Surface coil• highest signal at hotspot• high SNR at hotspot
Photo source: Joe Gati
Phased Array Coils• SNR of surface coils with the coverage of head coils• OR… faster parallel imaging• modern scanners come standard with 8- or 12-channel head coils and
capability for up to 32 channels
Photo Source: Technology Review
90-channel prototypeMass. General Hospital
Wiggins & Wald
12-channel coil 32-channel coil
32-channel head coilSiemens
Phased Array Coils
Source: Huettel, Song & McCarthy, 2004,Functional Magnetic Resonance Imaging
Voxel size• Bigger is better… to a point• Increasing voxel size signals summate, noise
cancels out• “Partial voluming”: If tissue is of different types, then
increasing voxel size waters down differences– e.g., gray and white matter in an anatomical– e.g., activated and unactivated tissue in a functional
Head Motion: Main ArtifactsHead motion Problems
time1 time2
1) Rim artifacts• hard to tell activation from artifacts• artifacts can work against activation
2) Region of interest moves•lose effects because you’re sampling outside ROI
Looking at the negative tail can help you identify artifacts
Playing a movie of slices over time helps you detect head motion
Motion Correction Algorithms
• Most algorithms assume a rigid body (i.e., that brain doesn’t deform with movement)
• Align each volume of the brain to a target volume using six parameters: three translations and three rotations
• Target volume: the functional volume that is closest in time to the anatomical image
x translation
z tr
ansl
atio
n
y tr
ansl
atio
n
pitch roll yaw
BVQX Motion Correction Options
• Motion correct .fmr file (2D) before any other preprocessing– Why?
• Align each volume to the volume closest to the anatomical– Why?
Analysis/fMRI 2D data preprocessing menu
Mass Motion Artifacts
• motion of any mass in the magnetic field, including the head, is a problem
headcoil
arm brace
gazegrasparatus
brace
Grasping and reaching data from
block designscirca 1998
The Challenges of fMRI: Artifacts at 4T
Time Course:
% S
ign
al C
ha
ng
e
-4
0
7
Time (seconds)15030 60 90 1200
Left Right Left Right Left
.60-.60
1.0
-1.0
r value
-0.4
0
0.6
Time (seconds)15030 60 90 1200
Mo
tion
De
tect
ed
(m
m o
r d
eg
ree
s)
Motion Correction Parameters
Even in the absence of head motion,
mass motion creates huge problems
30 s 30 s
Where is the signal correlated with the
mass position?
phantom(fluid-filled
sphere)
Culham, chapter in Cabeza & Kingstone, Handbook of Functional Neuroimaging of Cognition (2nd ed.), 2006
00
900
Motion Correction Algorithms
• Existing algorithms correct two of our three problems:1. Head motion leads to spurious activation
2. Regions of interest move over time
3. Motion of head (or any other large mass) leads to changes to field map
• Sometimes algorithms can introduce artifacts that weren’t there in the first place (Friere & Mangin, 2001, NeuroImage)
√
√
X
Head Restraint
Head Vise(more comfortable than it
sounds!)
Bite Bar
Often a whack of foam padding works as well as anything
Vacuum Pack
Thermoplastic mask
Prevention is the Best Remedy• Tell your subjects how to be good subjects
– “Don’t move” is too vague
• Make sure the subject is comfy going in– avoid “princess and the pea” phenomenon
• Emphasize importance of not moving at all during beeping– do not change posture– if possible, do not swallow– do not change posture– do not change mouth position– do not tense up at start of scan
• Discourage any movements that would displace the head between scans
• Do not use compressible head support
• For a summary of info to give first-time subjects, seehttp://defiant.ssc.uwo.ca/Jody_web/Subject_Info/firsttime_subjects.htm
Slice Scan Time Correction
The last slice is collected almost a full TR later (e.g., 3 s) than the first slice
Source: Brain Voyager documentation
Slice Scan Time Correction
Source: Brain Voyager documentation
• interpolates the data from each slice such that is is as if each slice had been acquired at the same time
Spatial Smoothing
Gaussian kernel• smooth each voxel by a Gaussian or normal function, such that the nearest neighboring voxels have the strongest weighting
Maximum
Half-Maximum
Full Width at Half-Maximum (FWHM)
FWHM Values• some smoothing: 4 mm• typically smoothing: 6-8 mm• heavy duty smoothing: 10 mm
3D Gaussian smoothing kernel
Effects of Spatial Smoothing on Activity
No smoothing 4 mm FWHM 7 mm FWHM 10 mm FWHM
Should you spatially smooth?
• Advantages– Increases Signal to Noise Ratio (SNR)
• Matched Filter Theorem: Maximum increase in SNR by filter with same shape/size as signal
– Reduces number of comparisons• Allows application of Gaussian Field Theory
– May improve comparisons across subjects• Signal may be spread widely across cortex, due to
intersubject variability
• Disadvantages– Reduces spatial resolution – Challenging to smooth accurately if size/shape of
signal is not known
Slide from Duke course
Linear Drift
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
Low and High Frequency Noise
Source: Smith chapter in Functional MRI: An Introduction to Methods
Physiological Noise
Respiration• every 4-10 sec (0.3 Hz)• moving chest distorts susceptibility
Cardiac Cycle• every ~1 sec (0.9 Hz)• pulsing motion, blood changes
Solutions• gating• avoiding paradigms at those frequencies
Order of Preprocessing Steps is Important
• Thought question: Why should you run motion correction before temporal preprocessing (e.g., linear trend removal)?
• If you execute all the steps together, software like Brain Voyager will execute the steps in the appropriate order
• Be careful if you decide to manually run the steps sequentially. Some steps should be done before others.
Take-Home Messages• Look at your data• Work with your physicist to minimize physical noise• Design your experiments to minimize physiological
noise• Motion is the worst problem: When in doubt, throw it
out• Preprocessing is not always a “one size fits all”
exercise
What affects SNR?Physical factors
PHYSICAL FACTORS SOLUTION & TRADEOFFThermal Noise (body & system) Inherent – can’t change
Magnet Strength
e.g. 1.5T 4T gives 2-4X increase in SNR
Use higher field magnet– additional cost and maintenance– physiological noise may increase
Coil
e.g., head surface coil gives ~2+X increase in SNR
Use surface coil– Lose other brain areas– Lose homogeneity
Voxel size
e.g., doubling slice thickness increases SNR by root-2
Use larger voxel size– Lose resolution
Sampling time Longer scan sessions– additional time, money and subject discomfort
Source: Doug Noll’s online tutorial
Head Motion: Main Artifacts
1. Head motion can lead to spurious activations or can hinder the ability to find real activations. • Severity of problem depends on correlation between motion and
paradigm
2. Head motion increases residuals, making statistical effects weaker.
3. Regions move over time– ROI analysis: ROI may shift– Voxelwise analyses: averages activated and nonactivated voxels
4. Motion of the head (or any other large mass) leads to changes to field map
5. Spin history effects• Voxel may move between excitation pulse and readout
507 89 154
119 171 83
179 117 53
663 507 89
520 119 171
137 179 117
A B C
Motion Intensity Changes
Slide modified from Duke course
Motion Spurious Activation at Edges
time1 time2
lateralmotion in x direction
motion in z direction
(e.g., padding sinks)
time 1 > time 2
time 1 < time 2
brainposition
statmap
Spurious Activation at Edges
• spurious activation is a problem for head motion during a run but not for motion between runs
Motion Increased Residuals
fMRI Signal
× 1
× 2
=
ResidualsDesign Matrix
++
“what we CAN
explain”
“what we CANNOT explain”
= +Betasx
“how much of it we CAN explain”
“our data” = +x
Statistical significance is basically a ratio of explained to unexplained variance
Regions Shift Over Time
• A time course from a selected region will sample a different part of the brain over time if the head shifts
• For example, if we define a ROI in run 1 but the head moves between runs 1 and 2, our defined ROI is now sampling less of the area we wanted and more of adjacent space
• This is a problem for motion between runs as well as within runs
time1 time2
Problems with Motion Correction
• lose information from top and bottom of image– possible solution: prospective motion correction
• calculate motion prior to volume collection and change slice plan accordingly
we’re missing data here
we have extra data here
Time 1 Time 2
Prospective Motion Correction• Siemens Prospective Acquisition
CorrEction (PACE)• shifts slices on-the-fly so that slice
planes follow motion• Siemens claims it improves data quality• Caution: unlike retrospective motion
correction algorithms, you can never get “raw” data
Source: Siemens
Why Motion Correction Can Be Suboptimal
1. Parts of brain (top or bottom slices) may move out of scanned volume (with z-direction motion or rotations)
2. Motion correction requires spatial interpolation, leads to blurring– fast algorithms (trilinear interpolation) aren’t as good as slow ones
(sinc interpolation) – Motion correction
Why Motion Correction Algorithms Can Fail
• Activation can be misinterpreted as motion– particularly problematic for least squares algorithms (Friere
& Mangin, 2001)
• Field distortions associated with moving mass (including mass of the head) can be misinterpreted as motion
Simulated activation
Spurious activation created by motion correction in SPM (least squares)
Mutual information algorithm in SPM has fewer problems
Friere & Mangin, 2001
Head Motion: Solution to Susceptibility
Solution:• one trial every 10 or 20 sec• fMRI signal is delayed ~5 sec
distinguish true activity from artifacts
IMPORTANT: Subject must remain in constant configuration between trials
0 5 10
Time (Sec)
fMRISignal
action
activityartifact
Different motions; different effectsDrift within run Movement
between runsUncorrelated abrupt movement within run
Correlated abrupt movement within a run
Motion correction
Spurious activations okay, corrected by LTR
okay minor problem huge problem can reduce problems
Increased residuals okay, corrected by LTR
okay problem problem can reduce problems; may be improved by including motion parameters as predictors of no interest
Regions move problem minor-major problem depending on size of movement
problem problem can reduce problems; if algorithm is fooled by physics artifacts, problem can be made worse by MC
Physics artifacts not such a problem because effects are gradual
okay problem huge problem can’t fix problem; may be misled by artifacts
Motion Correction Output
gradual motions are usually well-corrected
abrupt motions are more of a problem (esp if related to paradigm
SPM output
raw data
linear trend removal
motion corrected in SPM
Caveat: Motion correction can cause artifacts where there were none
Effect of Temporal Filtering
before
after
Source: Brain Voyager course slides
Spatial Distortions
Iso
cent
reIs
oce
ntre
+ 1
2 cm
Lengthwise Cross-sectionBefore Correction
Lengthwise Cross-SectionAfter Correction
Core Cross-sectionBefore Correction
Top
Bottom
A B C
D E F
Data Preprocessing Optionsreconstruction from raw k-space data
• frequency space real space
artifact screening• ensure the data is free from scanner and subject artifacts
vessel suppression• reduce the effects of large vessels (which are further away from activation than capillaries)
slice scan time correction• correct for sampling of different slices at different times
motion correction• correct for sampling of different slices at different times
spatial filtering • smooth the spatial data
temporal filtering • remove low frequency drifts (e.g., linear trends)• remove high frequency noise (not recommended because it increases temporal autocorrelation and artificially inflates statistics)
spatial normalization • put data in standard space (Talairach or MNI Space)
Vein, vein, go away
Source: Menon, 2002, Magn Reson Med
• large vessels tend to be consistently oriented (with respect to the cortex) whereas capillaries are randomly oriented • Ravi’s new algorithm uses this fact to estimate and remove the contribution of large vessels in the signal• this was verified by examining the time course of a voxel in a vein and a voxel in gray matter, with and without vessel suppression
voxel in vein
voxel in gray matter
raw data vessel suppression vessel selection
A Brief Primer on Fourier Analysis• Sine waves can be characterized by frequency and
amplitude
peak: high pointtrough: low point
frequency: number of cycles within a certain time or space (e.g., cycles per sec = Hz, cycles per cm)
amplitude: height of wave
phase: starting point
• (b) has same frequency as (a) but lower amplitude• (c) has lower frequency than (a) and (b)• (d) has same frequency and amplitude as (c) but different phase
peak
trough
amplitude
Source: DeValois & DeValois, Spatial Vision, 1990
Fourier Decomposition• Any wave form can be decomposed into a series of
sine waves
Frequency spectrum
Source: DeValois & DeValois, Spatial Vision, 1990
Temporal and Spatial Analysis
Temporal waveforms• e.g., sound waves• e.g., fMRI time courses
Spatial waveforms• can be one dimensional
(e.g., sine wave gratings in vision) or two dimensional (e.g., a 2D image)
• e.g., image analysis• e.g., an fMRI slice (k-
space)
Source: DeValois & DeValois, Spatial Vision, 1990
Fourier Synthesis
• centre = low frequencies
• periphery = high frequencies
• You can see how the image quality grows as we add more frequency information
Source: DeValois & DeValois, Spatial Vision, 1990
K-Space
Source: Traveler’s Guide to K-space (C.A. Mistretta)
What affects SNR?Physiological factors
Source: Doug Noll’s online tutorial
PHYSIOLOGICAL FACTORS SOLUTION & TRADEOFFHead (and body) motion Use experienced or well-warned subjects
– limits useable subjects
Use head-restraint system– possible subject discomfort
Post-processing correction– often incompletely effective– 2nd order effects– can introduce other artifacts
Single trials to avoid body motion
Cardiac and respiratory noise Monitor and compensate– hassle
Low frequency noise Use smart design
Perform post-processing filtering
BOLD noise (neural and vascular fluctuations) Use many trials to average out variability
Behavioral variations Use well-controlled paradigm
Use many trials to average out variability
BV Preprocessing Options
High pass filter•pass the high frequencies, block the low frequencies•a linear trend is really just a very very low frequency so LTR may not be strictly necessary if HP filtering is performed (though it doesn’t hurt)
Before High-pass
linear drift
~1/2 cycle/time course
~2 cycles/time course
After High-pass
BV Preprocessing Options
• Gaussian filtering– each time point gets averaged with adjacent time points– has the effect of being a low pass filter
• passes the low frequencies, blocks the high frequencies
– for reasons we will discuss later, I recommend AGAINST doing this
After Gaussian (Low Pass) filteringBefore Gaussian (Low Pass) filtering
Slice Scan Time Correction
• Slice scan time correction adjusts the timing of a slice corrected at the end of the volume so that it is as if it had been collected simultaneously with the first slice
original time courseshifted time course
Source: Brain Voyager documentation