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Coregistration and Normalisation

Coregistration and Normalisation. By Lieke de Boer & Julie Guerin. Presentation Overview. Recap: Steps of Preprocessing Coregistration Normalisation Smoothing Summary SPM Guidelines. Steps of Preprocessing. Realign & Unwarp Coregister Normalise Smooth. Raw data (fct). Raw data

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Coregistration and Normalisation

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  1. Coregistration and Normalisation By Lieke de Boer & Julie Guerin

  2. Presentation Overview • Recap: Steps of Preprocessing • Coregistration • Normalisation • Smoothing • Summary • SPM Guidelines

  3. Steps of Preprocessing Realign & Unwarp Coregister Normalise Smooth Raw data (fct) Raw data (str) MNI 1 fct fct fct str MNI fct fct fct 2 str fct MNI 4 4 is a blurry version of 3 3 fct str MNI

  4. Coregistration Normalisation& Smoothing

  5. Recap: 1. Realignment • Aligning all fMRI scans to reference scan • Linear transformations (translations and rotations) fct fct fct fct str MNI fct fct Translation Rotation

  6. Recap: 1. Unwarping • Non-linear transformations • Adjusts for deformations in magnetic field

  7. 2. Coregistration str fct MNI • Cross modal (structural/functional) realignment to the same native space. • Within-subjects Structural (high resolution) Functional (low resolution)

  8. Coregistration: Step 1 Translation Registration • Similar to realignment • Fitting source image (T1 structural) to reference image (T2 functional) Rotation

  9. Coregistration: Step 2 Reslicing/Interpolation • Estimating what value of intensity each voxel represents in a functional image. • Structural(high resolution; 1mm3) ≠ Functional(low resolution; 3mm3) • Reslicing estimates the intensity of surrounding voxels in functional scan so that functional voxels correspond with structural voxels.

  10. Coregistration: Step 2 (Continued) Methods of Interpolation: • Nearest Neighbour • Linear Interpolation • B-Spline Interpolation (Higher-Order Interpolation) zc ?

  11. Coregistration: Forming a Joint Histogram T2* (functional) histogram T1 (structural) histogram intensity # voxels # voxels intensity UN-Registered Joint Histogram Registered Joint Histogram

  12. 3. Normalisation fct str MNI Coregistered images Standardised space Talairach Atlas MNI Template

  13. Why Normalise? ≠ • Statistical power • Group analyses • Generalise findings/Representative • Cross-study comparisons (standard coordinate system)

  14. Normalisation • Aligning and warping to standardised space • Template fitting: • Right/Left • Anterior/Posterior • Superior/Inferior GOAL: voxel to voxel correspondence between brains of many subjects

  15. Normalisation: Optimisation Difficulties • Aim: to find an identical fit between the subjects’ brain and the template brain. • Reality: difficult to match brains when size/shape are so different • not structurally or functionally homologous • maximise similarity within reasonable limits/expectations.

  16. Normalisation: Affine Transformation Linear Transformations • Translations across axes • Rotations around axes • Scaling and zooming axes • Shearing or skewing, i.e. angle changes between pairs of axes

  17. Normalisation: Refine with Warping • Applies non-linear warping to images to match template. • Apply deformations/displacements to move voxels from original location to template location in multiple dimensions Affine Registration Non-linear Registration

  18. Normalisation: Risk of Overfitting • Warping is completely flexible and can therefore introduce unrealistic deformations = overfitting. Non-linear registration using regularisation. Non-linear registration without regularisation.

  19. Normalisation: Regularisation • Rather have less good match: compromise between reasonably good match & realistic deformation • Uses Bayesian framework: probability function of determining appropriate warp amounts. • Regularisation sets limits to warp parameters, and ensures voxels stay close to their neighbours.

  20. Normalisation: Segmentation • Different scanners, noise, artifacts, magnetic field properties, etc. prevent data from being uniformly and predictably adjusted to template.

  21. Segmentation • Tissue Probability Maps (TPM) from standard space help predict tissue differentiation in subjects. • Segmentationhelps correctly identify which tissue type the voxels of interest belong to.

  22. Normalisation: Generative Model • Each tissue type has Gaussian probability density function for intensity. • Goal is to get the best estimate of tissue probabilities. • Generative Model (Bayesian) – fitting a Gaussian Mixture Model (GMM) to the joint histogram

  23. 4. Gaussian Smoothing • Smoothing: adjusts for any residual differences and alignment errors. • Reduces signal to noise ratio. • Better spatial overlap • Better normally distributes data; enables statistical analyses • How: Convolutioncalculates a weighted average of neighbouring voxels -- each voxel gets replaced by a weighted average of itself and its neighbours.

  24. Keep In Mind: • fMRI analyses are not precise and are based on multiple data adjustments that ultimately alter the raw data substantially. • However, relatively reliable and robust inferences can be made when sample sizes are large enough, and when appropriate statistical analyses are performed.

  25. Summary fct fct 1. Realign & Unwarp fct str MNI fct fct fct 2. Coregister str fct MNI 3. Normalise & 4. Smooth fct str MNI 4 is a blurry version of 3 4

  26. SPM

  27. SPM

  28. SPM

  29. SPM

  30. Data = Structural file (batched, for all subjects) Tissue probability maps = 3 files: white matter, grey matter, CSF (Default) Masking image = exclude regions from spatial normalization (e.g. lesion) Parameter File = Click ‘Dependency’ (bottom right of same window) Images to Write = Co-registered functionals (same as in previous slide)

  31. SPM

  32. SPM

  33. References • http://www.ucl.ac.uk/stream/media/swatch?v=1d42446d1c34(Ged Ridgway’s preprocessing lecture) • SPM videos: http://www.fil.ion.ucl.ac.uk/spm/course/video/ • SPM Homepage: http://www.fil.ion.ucl.ac.uk/spm/ • Suz Prejawa • MfD Resources 2011-2012

  34. And thanks to Ged!

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