fmri Image Preprocessing Rick Hoge, Ph.D. Laboratoire de neuroimagerie vasculaire (LINeV) Centre de recherche de l institut universitaire de gériatrie de Montréal, Université de Montréal
Outline Motion correction Spatial filtering Distortion correction Physiological noise correction
Motion Correction serial realignment of all images to a target volume average over all volumes a single early or middle volume motion parameters can be used in subsequent temporal filtering
Image series with motion
Translations 2 1 AP Signal (au) 0 LR HF -1-2 0 100 200 300 400 Time (s)
Rotations 2 1 pitch Signal (au) 0 roll yaw -1-2 0 100 200 300 400 Time (s)
Realigned Series
MRM 31:283-291 (1994)
Stimulus-correlated motion
Artifactual activation
fmri Bite Bar Moana-Filho et al. BMC Neuroscience 2010
Software Support all major fmri software packages provide motion correction FSL SPM AFNI etc...
Spatial Filtering random noise in fmri data has a fairly high amplitude, comparable to functional changes we seek to detect averaging adjacent voxels can help increase the signal-to-noise ratio typically a 3D Gaussian smoothing kernel with width of around 5-6 mm is applied
Noise in fmri data 2 mm in-plane resolution
Dependence of SNR on spatial resolution 4 mm in-plane resolution
2 mm
4 mm
Noise drives residual error in GLM 724 720 Signal (au) 716 712 708 0 40 80 120 160 200 240 280 Time (s)
1200 1000 800 Signal (au) 600 400 2 mm 200 0 0 100 200 300 400 500 1200 1000 800 Signal (au) 600 400 4 mm 200 0 0 100 200 300 400 500
www.elsevier.com/locate/ynimg NeuroImage 32 (2006) 551 557 Effect of spatial smoothing on physiological noise in high-resolution fmri Christina Triantafyllou, Richard D. Hoge, and Lawrence L. Wald* MGH/MIT/HMS A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Dept. of Radiology, Mailcode 2301, Bldg. 149, 13th Street, Charlestown, MA 02129, USA
Image noise vs. temporal noise 724 720 Signal (au) 716 712 708 0 40 80 120 160 200 240 280 Time (s) Image Temporal
Image SNR and voxel volume 1.5 Tesla 3 Tesla 7 Tesla
Temporal SNR and voxel volume 1.5 Tesla 3 Tesla 7 Tesla
Temporal Filtering typically carried out as part of statistical modelling low frequency drift residual motion effects physiological noise
Temporal filtering example response terms motion terms drift terms
Physiological Noise motion cardiac pulsation respiratory movement
Image-Based Method for Retrospective Correction of Physiological Motion Effects in fmri: RETROICOR Gary H. Glover, 1 * Tie-Qiang Li, 1 and David Ress 2 Magnetic Resonance in Medicine 44:162 167 (2000)
Physiological noise in short-tr acquisition TR = 250 ms
Physiological noise in long-tr acquisition TR = 1 s
TR = 1 s
Reduction of residual error through physiological noise correction Raw K-Space correction Image-Space correction
www.elsevier.com/locate/ynimg NeuroImage 39 (2008) 680 692 Physiological noise modelling for spinal functional magnetic resonance imaging studies Jonathan C.W. Brooks, a, Christian F. Beckmann, e Karla L. Miller, b Richard G. Wise, c Carlo A. Porro, d Irene Tracey, a,b and Mark Jenkinson b
TR = 250 ms
Image distortion and dropout Microscopic: deoxygenated hemoglobin Macroscopic air-filled sinuses
Field Mapping image magnetization pattern at different echo times allows calculation of field offset based on phase accrual per unit time can be used to correct for distortion, but not dropout
MRI Data is Complex Magnitude (used) M xy Phase (discarded) = tan 1 M y M x
Phase image - short TE
Phase image - long TE
Distortion vs. Dropout distortion is associated with large echospacing values in EPI readouts dropout is associated with large voxel dimensions the following slides illustrate that they are independent processes (even though both are caused by field inhomogeneities)
EPI over MPRAGE 2 mm
3 mm
4 mm
5 mm
EPI over MPRAGE 2 mm
3 mm
4 mm
5 mm
EPI over MPRAGE 2 mm
3 mm
4 mm
5 mm
Distortion Correction use of parallel imaging techniques to minimize EPI readout duration always acquire a field map only use 128 matrix EPI scans if you really need them
Avoiding Dropout simplest way to minimize dropout is by reducing voxel dimensions will require more smoothing to recover SNR other advanced techniques such as Z-shim may be used always check your EPI coverage by overlaying raw EPI scans on an MPRAGE
Typical Order of Operations motion-correction spatial smoothing linear modeling temporal filtering of drift and and residual motion as nuisance regressors distortion correction applied to effect-size estimates etc. prior to group GLM
Questions?