Last Time. This Time. Thru-plane dephasing: worse at long TE. Local susceptibility gradients: thru-plane dephasing

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1 Motion Correction Last Time Mutual Information Optimiation Decoupling Translation & Rotation Interpolation SPM Example (Least Squares & MI) A Simple Derivation This Time Reslice example SPM Example : Remind ourselves what a typical functional image volume looks like: 1) Non-uniform Local Field Causes Local Dephasing Sagittal B o field map at 3T 5 water protons in different parts of the voxel 90 y slowest x fastest T = 0 T = TE Local susceptibility gradients: thru-plane dephasing Thru-plane dephasing: worse at long TE Bad for thick slice above frontal sinus 3T, TE = 21, 30, 40, 50, 60ms 1

2 Susceptibility in EPI can give either a compression or expansion Altering the direction kspace is transversed causes either local compression or expansion. Susceptibility Causes Image Distortion Use shortest possible encoding Echoplanar Image, Δθ α encode time α 1/BW choose your poison 3T whole body gradients Field near sinus 3T head gradients Encode time = 34, 26, 22, 17ms fmri Data analysis fmri Data analysis fmri time-series Reorient Slice order Unwarp Realignment Slice timing fmri time-series Realignment Normalisation fmri Data analysis Reorient Slice order Unwarp Slice timing Talairach Coordinate System to provide mechanism for comparing data across different labs/studies Talairach coordinate system (space) Atlas provided by Talairach and Tournoux Based on a French woman s brain Center (0,0,0 is on the anterior commisure) Line passes through the Anterior commisure Posterior commisure line (a.k.a., AC-PC line) Template 2

3 Talairach Coordinate System Individual brains are different shapes and sies How can we compare or average brains? Rotate brain into ACPC plane Corpus Callosum Find anterior commisure (AC) Talairach & Tournoux, 1988 squish or stretch brain into shoe box extract 3D coordinate (x, y, ) for each activation focus Pineal Body bent asparagus Fornix Find posterior commisure (PC) ACPC line = horiontal axis Note: official Tal se use top of AC and bottom of PC Source: Duvernoy, 1999 Note: That s TalAIRach, not TAILarach! Source: Brain Voyager course slides Deform brain into Talairach space Mark 8 points in the brain: anterior commisure posterior commisure front back top bottom (of temporal lobe) left right Squish or stretch brain to fit in shoebox of Tal system y y<0 AC=0 y>0 y>0 Extract 3 coordinates ACPC=0 y<0 x Talairach Atlas 3

4 Talairach Pros and Cons Left is what?!!! Advantages widespread system allows averaging of fmri data between subjects allows researchers to compare activation foci easy to use Disadvantages based on the squished brain of an elderly alcoholic woman (how representative is that?!) not appropriate for all brains (e.g., Japanese brains don t fit well) activation foci can vary considerably other landmarks like sulci may be more reliable Ignores left/right differences Neurologic (i.e. sensible) convention left is left, right is right L R - + x = 0 Radiologic (i.e. stupid) convention left is right, right is left R L Note: Make sure you know what your magnet and software are doing before publishing left/right info! Note: If you re really unsure which side is which, tape a vitamin E capsule to the one side of the subject s head. It will show up on the anatomical image. + x = 0 - Talairach vs MNI Talairach space Revised in spm96 using MNI brain Montreal Neurological Institute Based on 304 normal subjects More representative of population 10-15% larger than Talairach space Much confusion in the literature Must convert to Talairach to MNI space Meta-analyses Planning studies, regions of interest Different groups/software use different methods Manuscripts must specify the actual space used 4

5 Spatial Transformations: Warping Strategies Label-based (identifiable features) Identification of homologous structures (features, landmarks) between 2 images Find transformation that best superposes labeled points Non-label-based (no corresponding feature) Spatial transformation minimiing index of difference between images fmri time-series Realignment Slice order Normalisation Unwarp Slice timing Mean functional EPI image EPI template Structural image (T1) T1 template Template Mean functional image EPI template Coregister subject s mean EPI to structural T1 image Problems: - coreg is linear operation - EPI warping and - EPI distortion not accounted -Unless you have: field map correction -issues slow acquisition Structural image (T1) Normalie T1 to T1 Template T1 template Mean functional image EPI template Normalie mean EPI to EPI Template Structural image (T1) Good: - normaliation has linear and nonlinear components - EPI warping and - EPI distortion accounted for -Even better if you have: field map correction -issues slow acquisition Bad If mean EPI is different from EPI template T1 template normaliation can go wrong - tune normaliation - customie basis functions - create site template Data must be resliced into new MNI (Talairach space) Data must be interpolated sinc most likely best Must choose voxel sies to reslice into: Inputs were 3.44 x 3.44 x 5.00mm Default is 2.00 x 2.00 x 2.00 We use 3.00 x 3.00 x

6 Spatial Normalisation - Non-linear Deformations consist of a linear combination of smooth basis functions Deformation Field These are the lowest frequencies of a 3D discrete cosine transform (DCT) Algorithm simultaneously minimises * Mean squared difference between template and source image * Squared distance between parameters and their known expectation Original Warped Deformation field x' t 1(x,y,) y' = t 2(x, y,) ' t3 (x, y,) Template Jacobians Spatial Normalisation - Procedure Begin with affine registration Non-linear registration (about 1000 parameters) Jacobian Matrix (or just Jacobian ) x' x' x' j x y 11 j12 j13 y' y' y' J = j = 21 j22 j23 x y j31 j32 j33 ' ' ' x y Jacobian Determinant (or just Jacobian ) - relative volumes J = j11(j22j33 j23j32) j21(j12j33 j13j32) + j31(j12j23 j13j22) Affine registration Non-linear registration Affine vs Non-Rigid A Look at the transformation Affine vs Non-Rigid Affine Non-Rigid Affine 12 parameters Non-Rigid ~ 2000 parameters Average Anatomical Images from 10 Subjects displayed at 1.5x1.5x1.5 mm 6

7 SPM Example Next Time Filtering of Physiologic Signals, Drift, Etc 7