Jody Culham
Brain and Mind Institute
Department of Psychology
University of Western Ontario
http://www.fmri4newbies.com/
fMRI Data Quality Assurance
and Preprocessing
Last Update: January 28, 2013
Last Course: Psychology 9223, W2013, Western
Data Quality
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 without quality assurance, the
greater the chance of wonky results
Raw
Data
Big Black Box
of automated
software
Pretty pictures
Your favorite
fMRI software
Slide adapted from Mark Daley
Culham’s First Commandment:
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 Artifacts
Ghosts
Hardware Malfunctions
Spikes
Metallic Objects (e.g., hair tie)
Contrast and Contrast:Noise
T1
T2
High
Contrast:
Noise
Low
Contrast:
Noise
Why SNR Matters
Note: 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”
How Can You Tell the Difference?
• Test a phantom
No physiological noise!
A Map of Noise
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
• voxels with high variability shown in white
Effect of Field Strength on Signal and Noise
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
• Although raw SNR goes up with field strength, so does thermal
and physiological noise
• Thus there are diminishing returns for increases in field strength
Effect of Field Strength on Signal
Effect of Field Strength on Vascular Signals
Effect of Field Strength on Susceptibility
1.5 T
4.0 T
Coils
Head coil
Surface coil
• homogenous signal
• moderate SNR
• 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
12-channel coil
32-channel coil
90-channel prototype
Mass. General Hospital
Wiggins & Wald
32-channel head coil
Siemens
Photo Source: Technology Review
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
Sampling Time
• More samples  More confidence effects are real
What’s the most common source of
physiological noise?
Head Motion: Main Artifacts
Head motion Problems
1) Rim artifacts
•
•
hard to tell activation from artifacts
artifacts can work against activation
time1
 time2
Playing a movie of
slices over time
helps you detect
head motion
Looking at the
negative tail can
help you identify
artifacts
2) Region of interest moves
•lose effects because you’re sampling outside
ROI
BV Correction During Single Run
Head Motion: Good, Bad,…
run 1 run 2 run 3 run 4 run 5 run 6
Slide from Duke course
… and really, really ugly!
Slide from Duke course
Motion Correction Algorithms
roll
yaw
y translation
z translation
pitch
x translation
•
•
•
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
BVQX Motion Correction Options
Analysis/fMRI 2D data preprocessing menu
Align each volume to the volume closest in time to the anatomical
– Why?
Mass Motion Artifacts
• motion of any mass in the magnetic field, including the head,
is a problem
grasparatus
gaze
head
coil
brace
arm brace
Mass Motion Artifacts
Grasping and
reaching data from
block designs
circa 1998
Even in the absence
of head motion,
mass motion creates
huge problems
1.0
.60
-.60
Where
is the signal
phantom
correlated
with the
(fluid-filled
masssphere)
position?
30-1.0
s
30 s
r value
Motion Correction Parameters
7
900
Left
Left
Right
Right
Motion Detected
(mm or degrees)
% Signal Change
Time Course:
Left
0
-4
0 0
0
30
60
90
Time (seconds)
120
150
0.6
0
-0.4
0
30
60
90
120
150
Time (seconds)
Culham, chapter in Cabeza & Kingstone, Handbook of Functional Neuroimaging of Cognition (2nd ed.), 2006
Mass Motion Distort Magnetic Field
Barry et al., in press, Magnetic Resonance Imaging
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
X 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)
The Fridge Rule
• When it doubt, throw it out!
Head Restraint
Vacuum Pack
Head Vise
(more comfortable than it
sounds!)
Bite Bar
Thermoplastic mask
Often a whack of foam padding works as well as anything
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
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, see
http://defiant.ssc.uwo.ca/Jody_web/Subject_Info/firsttime_subjects.htm
Mock “0 T” Scanners
Data Preprocessing
Recommended Newish Book
Software
And you thought people were opinionated about
Mac vs. PC!
Table from Poldrack, Mumford & Nichols, 2011
Functional Data Organization
Image from Mark Daley
fMRI data has 4 dimensions
•3 Space: x (L-R), y (A-P), z (S-I)
•1 Time
•“Volume” = 3 spatial dimensions at
one point in time
Image from Poldrack et al., 2011
• Each value in our 4-dimensional
matrix is image intensity (blackwhite)
• The inherent scale may be arbitrary
or normalized
Sample Preprocessing Sequence
Figure from Poldrack, Mumford & Nichols, 2011
Disdaqs
• Discarded data acquisitions: trashed volumes at the beginning of a run
before the magnet has reached a steady state
• The scanner may throw out the disdaqs before it saves the data or it may
save them too, in which case you have to discard them in your software
• Sometimes it can take awhile for the subject to reach a steady state too -Startle response!
T1, T2, T2*
Image from Poldrack et al., 2011
• Different image types have different resolution, contrast
polarity, and distortions
• Nevertheless, we must ensure that the functional data
(T2*) align well with the anatomical data (typically T1)
• Use file headers to determine spatial alignment between
data types, then tweak if needed
Field Map Correction
• Remember we collect data in frequency space not image space
• Remember that our data collection assumes a homogeneous main
magnetic field
• If the magnetic field is not fully homogeneous, we get distortions in
frequency space and image space
• If we collect a “field map” we can correct these distortions
http://www.mathworks.com/matlabcentral/fileexchange/30853-field-mapping-toolbox
BV Preprocessing Options
Slice Order
NonInterleaved;
Descending
The first slice is collected almost a full
TR (e.g., 2 s) before the last slice
Problem with noninterleaved slices:
excitation of one slice may
carry over to next slice
Slice Order
Interleaved;
Descending
The first yellow slice is collected almost a full
TR (e.g., 2 s) before the last pink slice
Slice Scan Time Correction
Slice Scan Time Correction
• interpolates the data from each slice such that is is as
if each slice had been acquired at the same time
Source: Brain Voyager documentation
BV Preprocessing Options
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)
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8
FWHM = 6
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
“Why would you spend $4 million to
buy an MRI scanner and then blur the
data till it looked like PET?”
-- Ravi Menon
– Reduces spatial resolution
– Challenging to smooth accurately if size/shape of
signal is not known
Slide from Duke course
BV Preprocessing Options
Components of Time Course Data
Source: Smith chapter in Functional MRI: An Introduction to Methods
Linear Drift
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
BV Preprocessing Options
Before LTR:
After LTR:
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
Before Gaussian (Low Pass) filtering
After Gaussian (Low Pass) filtering
A Brief Primer on Fourier Analysis
• Sine waves can be characterized by frequency and
amplitude
amplitude
peak
trough
peak: high point
trough: 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
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 (kspace)
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
“Low-Pass” vs. “High-Pass”
Low-pass
•
pass the low frequencies
through the filter
•
remove the high
frequencies
•
you could also call this
temporal smoothing
High-pass
•
pass the low frequencies
through the filter
•
remove the high
frequencies
Find the “Sweet Spots”
• Even in a “resting state scan” (i.e., when subject isn’t
doing a task), certain frequencies are present
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
You want your paradigm frequency
to be in a “sweet spot” away from
the noise
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.
SSTC and 3DMC Interact
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
EXTRA SLIDES
What affects SNR?
Physical factors
PHYSICAL FACTORS
SOLUTION & TRADEOFF
Thermal Noise (body & system)
Inherent – can’t change
Magnet Strength
e.g. 1.5T  4T gives 2-4X increase in SNR
Use higher field magnet
Coil
e.g., head  surface coil gives ~2+X increase in
SNR
Use surface coil
Voxel size
e.g., doubling slice thickness increases SNR by root2
Use larger voxel size
Sampling time
Longer scan sessions
– additional cost and maintenance
– physiological noise may increase
– Lose other brain areas
– Lose homogeneity
– Lose resolution
– 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
Motion  Intensity Changes
A
B
C
507
89
154
663
507
89
119
171
83
520
119
171
179
117
53
137
179
117
Slide modified from Duke course
Motion  Spurious Activation at Edges
lateral
motion in
x direction
brain
position
stat
map
motion in
z direction
(e.g., padding sinks)
time1

time2
time 1 > time 2
time 1 < time 2
Spurious Activation at Edges
• spurious
activation is a
problem for head
motion during a
run but not for
motion between
runs
Motion  Increased Residuals
× 1
=
+
+
× 2
fMRI Signal
“our data”
=
=
Design Matrix x Betas
“what we
CAN
explain”
x
“how much of
it we CAN
explain”
+
Residuals
+
“what we
CANNOT
explain”
Statistical significance is basically a ratio of
explained to unexplained variance
Regions Shift Over Time
time1

time2
• 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
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
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
Spurious activation created Mutual information
by motion correction in
algorithm in SPM has
SPM (least squares)
fewer problems
Simulated activation
Friere & Mangin, 2001
Solution to Mass Motion Artifcact
(e.g., speech, hand actions)
Solution:
• employ a slow event-related design
with one trial every ~14 s
• artifacts occur at the time of motion
• BOLD response is delayed ~5 sec
action
artifact activity
fMRI
Signal
0
IMPORTANT: Subject must remain in
constant configuration between trials
5
10
Time (Sec)
Different motions; different effects
Drift within run
Movement
between runs
Uncorrelated
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
Effect of Temporal Filtering
before
after
Source: Brain Voyager course slides
Trial-to-trial variability
Single trials
Average of all trials from 2 runs
Spatial Distortions
Core Cross-section
Before Correction
A
Lengthwise Cross-section
Before Correction
B
Lengthwise Cross-Section
After Correction
C
Isocentre
Top
Isocentre + 12 cm
Bottom
D
E
F
Homogeneity Correction
Data Preprocessing Options
reconstruction 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)
What affects SNR?
Physiological factors
PHYSIOLOGICAL FACTORS
SOLUTION & TRADEOFF
Head (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
Source: Doug Noll’s online tutorial
Slice Scan Time Correction
original time course
shifted time course
• 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
Source: Brain Voyager documentation
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.
e.g., =4, SD=2.1
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)]
When citing SNR, state which denominator you used.
Head coil should have SNR > 50:1
Surface coil should have SNR > 100:1
Source: Joe Gati, personal communication
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