1. fMRI Data Quality Assurance and Preprocessing
Jody Culham
Department of Psychology
University of Western Ontario
fMRI Data Quality Assurance and Preprocessing
Last Update: November 29, 2008
fMRI Course, Louvain, Belgium

2. The Black Box Big Black Box of automated software Raw Data
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 Look at the movies
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.

4. Hardware Malfunctions
Sample Artifacts
Ghosts
Hardware Malfunctions
Metallic Objects (e.g., hair tie)
Spikes

5. 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.
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
Head coil should have SNR > 50:1
Surface coil should have SNR > 100:1
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 voxels with high variability shown in white
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 Surface coil homogenous signal moderate SNR
highest signal at hotspot
high SNR at hotspot
Photo source: Joe Gati

11. 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
90-channel prototype
Mass. General Hospital
Wiggins & Wald
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

13. 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

14. 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 you’re sampling outside ROI

16. Head Motion: Good, Bad,…
Slide from Duke course

17. … and catastrophically bad
Slide from Duke course

18. Motion Correction Algorithms
pitch
roll
yaw
z translation
y translation
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

19. 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

20. Mass Motion Artifacts
motion of any mass in the magnetic field, including the head, is a problem
head
coil
arm brace
gaze
grasparatus
brace

21. 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
.60
-.60
1.0
-1.0
r value
phantom
(fluid-filled
sphere)
30 s
30 s
Where is the signal correlated with the mass position?
Time Course:
% Signal Change -4 7
Time (seconds)
150 30 60 90
120
Left
Right
-0.4
0.6
Time (seconds)
150 30 60 90
120
Motion Detected (mm or degrees)
Motion Correction Parameters
900
Culham, chapter in Cabeza & Kingstone, Handbook of Functional Neuroimaging of Cognition (2nd ed.), 2006

22. 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 weren’t there in the first place (Friere & Mangin, 2001, NeuroImage) √ √ X

23. The Fridge Rule
When it doubt, throw it out!

24. (more comfortable than it sounds!)
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

25. 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 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

26. BV Preprocessing Options

27. 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

28. 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

29. Slice Scan Time Correction
Interleaved
Source: Brain Voyager documentation

30. BV Preprocessing Options

31. 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

32. Effects of Spatial Smoothing on Activity
4 mm FWHM
7 mm FWHM
10 mm FWHM
No smoothing
LO Localizer, 1 run, mt.10_06_03_10_MAG_THPGLMF2c_SD3DSSXXXXXmm.fmr, Intact > Scrambled, t>3.5

33. 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

34. BV Preprocessing Options

35. Linear Drift
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

36. Low and High Frequency Noise
Source: Smith chapter in Functional MRI: An Introduction to Methods

37. 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.

39. 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

40. EXTRA SLIDES

41. What affects SNR? 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
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

42. 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

43. Motion  Intensity ChangesB C
Slide modified from Duke course

44. Motion  Spurious Activation at Edges
lateral
motion in
x direction
motion in
z direction
(e.g., padding sinks)
brain
position
time1
time2 
time 1 > time 2
time 1 < time 2
stat
map

45. Spurious Activation at Edges
spurious activation is a problem for head motion during a run but not for motion between runs

46. Motion  Increased Residuals
× 1 = + +
× 2 =
fMRI Signal
Design Matrix x
Betas +
Residuals
“what we CAN explain”
“how much of it we CAN explain”
“what we CANNOT explain” =
“our data” x +
Statistical significance is basically a ratio of explained to unexplained variance

47. 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
we’re 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) aren’t as good as slow ones (sinc interpolation)
Motion correction

51. 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

52. Head Motion: Solution to Susceptibility
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 5 10
Time (Sec)
fMRI
Signal
action
activity
artifact

53. 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
problem
can reduce problems; may be improved by including motion parameters as predictors of no interest
Regions move
minor-major problem depending on size of movement
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
can’t fix problem; may be misled by artifacts

54. 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

55. Effect of Temporal Filtering
before
after
Source: Brain Voyager course slides

56. Trial-to-trial variability
Single trials
Average of all trials from 2 runs

57. Time Course Filtering

58. Spatial Distortions Isocentre Isocentre + 12 cm
Lengthwise Cross-section
Before Correction
Lengthwise Cross-Section
After Correction
Core Cross-section
Top
Bottom A B C D E F

59. Homogeneity Correction

60. 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
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
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
raw data
vessel suppression
vessel selection
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

64. 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

65. 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

66. K-Space
Source: Traveler’s Guide to K-space (C.A. Mistretta)

67. What affects SNR? 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
Source: Doug Noll’s online tutorial

68. BV Preprocessing Options
Before LTR:
After LTR:

69. 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
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
Before Gaussian (Low Pass) filtering
After Gaussian (Low Pass) filtering

71. 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