fMRI Data Quality Assurance and Preprocessing

1
fMRI Data Quality Assuranceand Preprocessing
Jody Culham Department of Psychology University
of Western Ontario
http//www.fmri4newbies.com/
Last Update November 29, 2008 fMRI Course,
Louvain, Belgium
2
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

Big Black Box of automated software
Raw Data
Pretty pictures
3
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 didnt get reversed
• Make sure functionals line up well with
  anatomicals following all transformations
• Think as you go. Investigate suspicious
  patterns.

4
Sample Artifacts
Ghosts
Metallic Objects (e.g., hair tie)
Hardware Malfunctions
Spikes
5
Calculating SignalNoise 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, SD2.1
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.
e.g., ? 200
WARNING! computation of SNR is complicated for
phased array coils WARNING! some software
might recalibrate intensities so its best to do
computations on raw data
Head coil should have SNR gt 501 Surface coil
should have SNR gt 1001
Source Joe Gati, personal communication
6
Why SNR Matters
Note This SNR level is not based on the formula
given
Huettel, Song McCarthy, 2004, Functional
Magnetic Resonance Imaging
7
Sources of Noise

• Physical noise
• Blame the magnet, the physicist, or the laws of
  physics
• Physiological noise
• Blame the subject

8
A Map of Noise
Huettel, Song McCarthy, 2004, Functional
Magnetic Resonance Imaging

• voxels with high variability shown in white

9
Field Strength
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

10
Coils

• Head coil
• homogenous signal
• moderate SNR

• Surface coil
• highest signal at hotspot
• high SNR at hotspot

Photo source Joe Gati
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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
32-channel head coil Siemens
Photo Source Technology Review
12
Phased Array Coils
Source Huettel, Song McCarthy,
2004, Functional Magnetic Resonance Imaging
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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

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Sampling Time

• More samples ? More confidence effects are real

15
Head Motion Main Artifacts
Head motion Problems

• 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 youre sampling outside ROI

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Head Motion Good, Bad,
Slide from Duke course
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and catastrophically bad
Slide from Duke course
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Motion Correction Algorithms
pitch
roll
yaw
z translation
y translation
x translation

• Most algorithms assume a rigid body (i.e., that
  brain doesnt 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

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BVQX Motion Correction Options
Analysis/fMRI 2D data preprocessing menu

• Motion correct .fmr file (2D) before any other
  preprocessing
• Why?
• Align each volume to the volume closest to the
  anatomical
• Why?

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Mass Motion Artifacts

• motion of any mass in the magnetic field,
  including the head, is a problem

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The Challenges of fMRI Artifacts at 4T
Grasping and reaching data from block
designs circa 1998
Even in the absence of head motion, mass motion
creates huge problems
phantom (fluid-filled sphere)
Where is the signal correlated with the mass
position?
900
0
Culham, chapter in Cabeza Kingstone, Handbook
of Functional Neuroimaging of Cognition (2nd
ed.), 2006
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Motion Correction Algorithms

• Existing algorithms correct two of our three
  problems
• Head motion leads to spurious activation
• Regions of interest move over time
• Motion of head (or any other large mass) leads to
  changes to field map
• Sometimes algorithms can introduce artifacts that
  werent there in the first place (Friere
  Mangin, 2001, NeuroImage)

v
v
X
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The Fridge Rule

• When it doubt, throw it out!

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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
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Prevention is the Best Remedy

• Tell your subjects how to be good subjects
• Dont 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/fi
  rsttime_subjects.htm

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BV Preprocessing Options
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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
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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
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Slice Scan Time Correction
Interleaved
Source Brain Voyager documentation
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BV Preprocessing Options
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Spatial Smoothing

• Gaussian kernel
• smooth each voxel by a Gaussian or normal
  function, such that the nearest neighboring
  voxels have the strongest weighting

• FWHM Values
• some smoothing 4 mm
• typically smoothing 6-8 mm
• heavy duty smoothing 10 mm

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Effects of Spatial Smoothing on Activity
No smoothing
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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
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BV Preprocessing Options
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Linear Drift
Huettel, Song McCarthy, 2004, Functional
Magnetic Resonance Imaging
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Low and High Frequency Noise
Source Smith chapter in Functional MRI An
Introduction to Methods
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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

38
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.

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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

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EXTRA SLIDES
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What affects SNR?Physical factors
Source Doug Nolls online tutorial
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Head Motion Main Artifacts

• 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
• Head motion increases residuals, making
  statistical effects weaker.
• Regions move over time
• ROI analysis ROI may shift
• Voxelwise analyses averages activated and
  nonactivated voxels
• Motion of the head (or any other large mass)
  leads to changes to field map
• Spin history effects
• Voxel may move between excitation pulse and
  readout

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Motion ? Intensity Changes
A
B
C
Slide modified from Duke course
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Motion ? Spurious Activation at Edges
lateral motion in x direction
motion in z direction (e.g., padding sinks)
brain position
stat map
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Spurious Activation at Edges

• spurious activation is a problem for head motion
  during a run but not for motion between runs

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Motion ? Increased Residuals
?1



?2

fMRI Signal
Residuals
Design Matrix

Betas
x
what we CAN explain
what we CANNOT explain
how much of it we CAN explain


x
our data
Statistical significance is basically a ratio of
explained to unexplained variance
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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
48
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

were missing data here
we have extra data here
Time 1
Time 2
49
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
50
Why Motion Correction Can Be Suboptimal

• Parts of brain (top or bottom slices) may move
  out of scanned volume (with z-direction motion or
  rotations)
• Motion correction requires spatial interpolation,
  leads to blurring
• fast algorithms (trilinear interpolation) arent
  as good as slow ones (sinc interpolation)
• Motion correction

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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 by motion correction
in SPM (least squares)
Mutual information algorithm in SPM has fewer
problems
Friere Mangin, 2001
Simulated activation
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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

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Different motions different effects
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Motion Correction Output
SPM output
raw data
gradual motions are usually well-corrected
linear trend removal
abrupt motions are more of a problem (esp if
related to paradigm
motion corrected in SPM
Caveat Motion correction can cause artifacts
where there were none
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Effect of Temporal Filtering
before
after
Source Brain Voyager course slides
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Trial-to-trial variability
Single trials
Average of all trials from 2 runs
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Time Course Filtering
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Spatial Distortions
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Homogeneity Correction
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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)

61
Vein, vein, go away

• large vessels tend to be consistently oriented
  (with respect to the cortex) whereas capillaries
  are randomly oriented
• Ravis 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
Source Menon, 2002, Magn Reson Med
62
A Brief Primer on Fourier Analysis

• Sine waves can be characterized by frequency and
  amplitude

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
amplitude
peak
trough

• (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
63
Fourier Decomposition

• Any wave form can be decomposed into a series of
  sine waves

Frequency spectrum
Source DeValois DeValois, Spatial Vision, 1990
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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
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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
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K-Space
Source Travelers Guide to K-space (C.A.
Mistretta)
67
What affects SNR?Physiological factors
Source Doug Nolls online tutorial
68
BV Preprocessing Options
Before LTR
After LTR
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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 doesnt hurt)

Before High-pass
After High-pass
linear drift
1/2 cycle/time course
2 cycles/time course
70
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) filtering
Before Gaussian (Low Pass) filtering
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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