Unwarping

1. Unwarping Irma Kurniawan MFD 2008

2. Overview: potential pre-processing steps(applicable for a typical cognitive fMRI experiments) 1. Realignment (within-modality) Realign + Unwarping (EPI with fieldmaps) 2. Between-modality Coregistration Coreg + estimate (T1 to EPI) 3. Segmentation (T1, to obtain seg_sn*.mat) 4. Normalisation (to stereotactic space) Normalise + write (EPI and T1 with seg_sn*.mat) 5. Smoothing (all EPI) 1. Realignment (within-modality) Realign + Unwarping (EPI with fieldmaps) 2. Between-modality Coregistration Coreg + estimate (T1 to EPI) 3. Segmentation (T1, to obtain seg_sn*.mat) 4. Normalisation (to stereotactic space) Normalise + write (EPI and T1 with seg_sn*.mat) 5. Smoothing (all EPI)

3. Why unwarp? This effect is called Movement-by-distortion interaction. • EPI images are not accurate reproductions of brain shape • Different tissues have different magnetic susceptibilities • These geometric distortions are most noticeable near cavities/ air-tissue interfaces (e.g. OFC, medial temporal lobe) • When inhomogeneities are present in the field, the signal will not change linearly with subject position this is what’s taken care of when unwarping

4. No correction Correction by covariation Correction by Unwarp tmax=13.38 tmax=5.06 tmax=9.57 Advantages of incorporating unwarping in pre-processing • One could include the movement parameters as confounds in the statistical model of activations. • However, this may remove activations of interest if they are correlated with the movement.

5. How do we fix movement-by-distortion interaction? • We estimate how the deformations change when the subject changes position. • A deformation field indicates the directions and magnitudes of location deflections throughout the field (B0) with respect to the real object. • A deformation field can be thought of as little vectors at each position in space showing how that particular location has been deflected. • This is done by formulating the inverse problem, i.e. given the observed variance (after realignment) and known (estimated) movements, we can estimate how deformations change with subject movement. • The underlying model for this is so restricted, so experimentally induced variance (the good variance) is preserved. To do this we iterate: 1) estimate movement parameters, 2) estimate deformation fields, 1) re-estimate movement …and so on..

6. How does it do it? The field B0, which changes as a function of displacement ∆θ, ∆φ, can be modelled by the first two terms of a Taylor expansion B0(, ) = B0 (, ) + [(δB0/ δ)  + (δB0/ δ)] The ‘static’ deformation field, which is the same throughout the time series. Calculated using ‘Fieldmap’ in SPM Changes in the deformation field with subject movement. Estimated via iteration. Procedure in UNWARP.

7. Applying the deformation field to the image Once the deformation field has been modelled over time, the time-variant field is applied to the image. This allows us to assume that voxels over time are corresponding to the same parts of the brain, increasing the sensitivity of our analysis.

8. In practice.. In practice, rather than generating a statistical field map for every image in the EPI data set, we compute how one map is warped over subsequent scans.

9. Slight digression: using fieldmaps Before realign + unwarp step: 1. In scanner: acquire 1 set of fieldmaps for each subject (regardless of number of runs per subject) 2. After scanning: convert fieldmaps into img files (DICOM import or TBR trajectories), A) use fieldmap toolbox OR B) use script on matlab to run Preprocess_fieldmap.m to create vdm files for each run for each subject. * You need to enter various default values in this step, so check physics wiki for the defaults appropriate to your scanner type and scanning sequence 3. Enter vdm* files with EPI images into realign + unwarp step

10. Series number Step 2B: fieldmap toolbox on SPM8 • If using toolbox, you need to load the right phase and mag images. • phase: one for which there’s only one file with that series number • Mag: the first file of the two files with the same series number

11. Realign + unwarp on spm8 Click ‘RUN’ • Click on ‘new session’ as many times as your session numbers • ‘images’ = EPI data fM*.img, ~100s images • ‘phase map’ = vdm*.img • Do this for each session • The rest is default, but (if you’re scanning at FIL): • Estimation options  ‘Wrap in Y’ • Same goes for ‘Unwarp and reslicing options’

12. Consider before using unwarp… Subject movements are quite small With the latest scanners, distortions are typically quite small, and distortion-by-motion interactions even smaller Small distortions result from: Fast gradients Low field (i.e. <3T) Low resolution (smoothing) If your setup is as above, you may not need unwarping..

13. Summary • Movement-by-distortion interaction can be accommodated during realignment using “unwarp” in SPM8 • WARNING!! UNWARP can be computationally intensive, and therefore take a long time!

14. References SPM5 manual ch.3 Slides by Antoinette Nicolle (MfD 2007) Slides by Mary Summers (MfD 2006) John Ashburner’s slides http://www.fil.ion.ucl.ac.uk/spm/course/#slides Wiki physics page by Chloe Hutton

18. In the defaults there is also an option to include The advantages of using Unwarp will also depend strongly on the specifics of the scanner and sequence by which your data has been acquired. When using the latest generation scanners distortions are typically quite small, and distortion-by-movement interactions consequently even smaller. A small check list in terms of distortions is a) Fast gradients->short read-out time->small distortions b) Low field (i.e. <3T)->small field changes->small distortions c) Low res (64x64)->short read-out time->small distortions d) SENSE/SMASH->short read-out time->small distortions If you can tick off all points above chances are you have minimal distortions to begin with and you can say ”sod Unwarp” (but not to our faces!). 3.1 Data Jacobian intensity modulation • Intended to correct appreciable movement in data (>1deg). • It minimises total (across the image volume) variance in the data set. Because the susceptibility-by-movement interaction effects are localised to ”problem” areas, the reduction of ‘unwanted variance’ in e.g. frontal-medial, orbitofrontal cortices, and temporal lobes can be quite dramatic (>90). • In theory: a brilliant idea, in practice: Not so. • Default: NO