fmri data quality assurance and preprocessing last update: january 18, 2012 last course: psychology...
TRANSCRIPT
![Page 1: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/1.jpg)
fMRI Data Quality Assuranceand Preprocessing
http://www.fmri4newbies.com/
Last Update: January 18, 2012Last Course: Psychology 9223, W2010, University of Western Ontario
Jody CulhamBrain and Mind Institute
Department of PsychologyUniversity of Western Ontario
![Page 2: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/2.jpg)
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
RawData
Big Black Box of automated
software
Pretty pictures
![Page 3: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/3.jpg)
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
![Page 4: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/4.jpg)
Sample ArtifactsGhosts
Spikes
Metallic Objects (e.g., hair tie)Hardware Malfunctions
![Page 6: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/6.jpg)
Why SNR MattersNote: This SNR level is not based on the formula given
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
![Page 7: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/7.jpg)
Sources of Noise
Physical noise• “Blame the magnet, the physicist, or the laws of physics”
Physiological noise• “Blame the subject”
![Page 8: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/8.jpg)
How Can You Tell the Difference?
• Test a phantom -- No physiological noise!
![Page 9: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/9.jpg)
A Map of Noise
• voxels with high variability shown in white
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
![Page 10: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/10.jpg)
Effect of Field Strength on Signal and Noise
• 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
![Page 14: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/14.jpg)
Coils
Head coil• homogenous signal• moderate SNR
Surface coil• highest signal at hotspot• high SNR at hotspot
Photo source: Joe Gati
![Page 15: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/15.jpg)
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
![Page 16: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/16.jpg)
Phased Array Coils
Source: Huettel, Song & McCarthy, 2004,Functional Magnetic Resonance Imaging
![Page 17: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/17.jpg)
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
![Page 19: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/19.jpg)
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
![Page 22: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/22.jpg)
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
![Page 23: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/23.jpg)
BVQX Motion Correction Options
Align each volume to the volume closest in time to the anatomical– Why?
Analysis/fMRI 2D data preprocessing menu
![Page 24: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/24.jpg)
Mass Motion Artifacts• motion of any mass in the magnetic field, including the head,
is a problem
headcoil
arm brace
gazegrasparatus
brace
![Page 25: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/25.jpg)
Grasping and reaching data from
block designscirca 1998
Mass Motion Artifacts
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
![Page 26: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/26.jpg)
Mass Motion Distort Magnetic Field
Barry et al., in press, Magnetic Resonance Imaging
![Page 27: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/27.jpg)
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
![Page 29: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/29.jpg)
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
![Page 30: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/30.jpg)
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
![Page 31: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/31.jpg)
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
![Page 33: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/33.jpg)
Disdaqs• Discarded data acquisitions: trashed volumes at the beginning of a run
before the magnet has reached a steady state• Sometimes it can take awhile for the subject to reach a steady state too --
Startle response!
![Page 35: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/35.jpg)
Slice Scan Time Correction
The first slice is collected almost a full TR (e.g., 3 s) before the last slice
Source: Brain Voyager documentation
Non-Interleaved
![Page 38: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/38.jpg)
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
![Page 40: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/40.jpg)
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
![Page 41: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/41.jpg)
Effects of Spatial Smoothing on Activity
No smoothing 4 mm FWHM 7 mm FWHM 10 mm FWHM
![Page 42: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/42.jpg)
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
“Why would you spend $4 million to buy an MRI scanner and then blur the data till it looked like PET?”
-- Ravi Menon
![Page 44: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/44.jpg)
Linear Drift
Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
![Page 45: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/45.jpg)
Components of Time Course Data
Source: Smith chapter in Functional MRI: An Introduction to Methods
![Page 47: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/47.jpg)
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
![Page 48: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/48.jpg)
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
![Page 49: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/49.jpg)
Find the “Sweet Spots”
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
![Page 50: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/50.jpg)
Macro- vs. micro- vasculature
Macrovasculature:vessels > 25 m radius(cortical and pial veins) linear and oriented cause both magnitude and phase changes
Microvasculature:vessels < 25 m radius(venuoles and capillaries) randomly oriented cause only magnitude changes
Capillary beds within the cortex
Source: Duvernoy, Delon & Vannson, 1981, Brain Research Bulletin
![Page 51: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/51.jpg)
“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 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
![Page 52: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/52.jpg)
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.
![Page 53: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/53.jpg)
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
![Page 55: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/55.jpg)
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
![Page 56: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/56.jpg)
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
![Page 58: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/58.jpg)
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
![Page 59: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/59.jpg)
Spurious Activation at Edges
• spurious activation is a problem for head motion during a run but not for motion between runs
![Page 60: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/60.jpg)
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
![Page 61: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/61.jpg)
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
![Page 62: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/62.jpg)
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
![Page 63: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/63.jpg)
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
![Page 64: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/64.jpg)
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
![Page 65: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/65.jpg)
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
![Page 66: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/66.jpg)
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
![Page 67: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/67.jpg)
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
![Page 68: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/68.jpg)
Effect of Temporal Filtering
before
after
Source: Brain Voyager course slides
![Page 71: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/71.jpg)
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
![Page 73: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/73.jpg)
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)
![Page 74: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/74.jpg)
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
![Page 75: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/75.jpg)
Fourier Decomposition• Any wave form can be decomposed into a series of
sine waves
Frequency spectrum
Source: DeValois & DeValois, Spatial Vision, 1990
![Page 76: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/76.jpg)
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
![Page 77: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/77.jpg)
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
![Page 78: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/78.jpg)
K-Space
Source: Traveler’s Guide to K-space (C.A. Mistretta)
![Page 79: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/79.jpg)
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
![Page 80: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/80.jpg)
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
![Page 81: FMRI Data Quality Assurance and Preprocessing Last Update: January 18, 2012 Last Course: Psychology 9223, W2010, University](https://reader035.vdocuments.net/reader035/viewer/2022062409/5697bffc1a28abf838cc152e/html5/thumbnails/81.jpg)
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