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

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

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.

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

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

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”

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

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

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 90-channel prototype Mass. General Hospital Wiggins & Wald 12-channel coil 32-channel coil 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

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

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

… and catastrophically bad Slide from Duke course

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

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

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

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

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

The Fridge Rule When it doubt, throw it out!

(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

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 http://defiant.ssc.uwo.ca/Jody_web/Subject_Info/firsttime_subjects.htm

BV Preprocessing Options

Slice Scan Time Correction The last slice is collected almost a full TR later (e.g., 3 s) than the first slice Source: Brain Voyager documentation

Slice Scan Time Correction 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

Slice Scan Time Correction Interleaved 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) FWHM Values some smoothing: 4 mm typically smoothing: 6-8 mm heavy duty smoothing: 10 mm 3D Gaussian smoothing kernel

Effects of Spatial Smoothing on Activity 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

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

BV Preprocessing Options

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

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

Physiological Noise Respiration every 4-10 sec (0.3 Hz) moving chest distorts susceptibility Cardiac Cycle every ~1 sec (0.9 Hz) pulsing motion, blood changes Solutions gating avoiding paradigms at those frequencies

Order of Preprocessing Steps is Important Thought question: Why should you run motion correction before temporal preprocessing (e.g., linear trend removal)? If you execute all the steps together, software like Brain Voyager will execute the steps in the appropriate order Be careful if you decide to manually run the steps sequentially. Some steps should be done before others.

Take-Home Messages Look at your data Work with your physicist to minimize physical noise Design your experiments to minimize physiological noise Motion is the worst problem: When in doubt, throw it out Preprocessing is not always a “one size fits all” exercise

EXTRA SLIDES

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

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

Motion  Intensity Changes B C Slide modified from Duke course

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

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 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

Regions Shift Over Time A time course from a selected region will sample a different part of the brain over time if the head shifts For example, if we define a ROI in run 1 but the head moves between runs 1 and 2, our defined ROI is now sampling less of the area we wanted and more of adjacent space This is a problem for motion between runs as well as within runs  time1 time2

Problems with Motion Correction lose information from top and bottom of image possible solution: prospective motion correction calculate motion prior to volume collection and change slice plan accordingly we’re missing data here we have extra data here Time 1 Time 2

Prospective Motion Correction Siemens Prospective Acquisition CorrEction (PACE) shifts slices on-the-fly so that slice planes follow motion Siemens claims it improves data quality Caution: unlike retrospective motion correction algorithms, you can never get “raw” data Source: Siemens

Why Motion Correction Can Be Suboptimal 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

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

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

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

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

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

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

Time Course Filtering

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

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 spatial filtering smooth the spatial data temporal filtering remove low frequency drifts (e.g., linear trends) remove high frequency noise (not recommended because it increases temporal autocorrelation and artificially inflates statistics) spatial normalization put data in standard space (Talairach or MNI Space)

Vein, vein, go away 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

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

Fourier Decomposition Any wave form can be decomposed into a series of sine waves Frequency spectrum Source: DeValois & DeValois, Spatial Vision, 1990

Temporal and Spatial Analysis Temporal waveforms e.g., sound waves e.g., fMRI time courses Spatial waveforms can be one dimensional (e.g., sine wave gratings in vision) or two dimensional (e.g., a 2D image) e.g., image analysis e.g., an fMRI slice (k-space) Source: DeValois & DeValois, Spatial Vision, 1990

Fourier Synthesis centre = low frequencies periphery = high frequencies You can see how the image quality grows as we add more frequency information Source: DeValois & DeValois, Spatial Vision, 1990

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

What affects SNR? Physiological factors 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

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 After High-pass linear drift ~1/2 cycle/time course ~2 cycles/time course

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

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