Coregistration and Spatial Normalisation

1. Coregistration and Spatial Normalisation Ana Saraiva Britt Hoffland

2. Statistical Parametric Map Design matrix fMRI time-series kernel Motion correction Smoothing General Linear Model (Co-registration and) Spatial normalisation Parameter Estimates Standard template Overview

3. PET T1 MRI • Co-registration • Between modality co-registration

4. Why is between-modality co-registration useful? • Significant advantages in research and clinical settings

5. Principles of co-registration Registration  Transformation 6 Parameters for motion correction

6. T1 Transm T2 PD PET EPI Different for between-modality coregistration • Shape • Signal intensities

7. Between modality registration • Manually (homologous landmarks) • I via templates • II mutual information

8. 12 parameter affine transformations Templates conform to the same anatomical space Simultaneous registration Via Templates

9. 1. Affine Registration • 12 parameter affine transform • 3 translations • 3 rotations • 3 zooms • 3 shears • Fits overall shape and size • Algorithm simultaneously minimises • Mean-squared difference between template and source image • Squared distance between parameters and their expected values (regularisation)

10. However… • Image MRI  Template MRI Scaling/shearing parameters Rigid body transformation parameters • Image PET  Template PET

11. Priors: Image: GM WM CSF Brain/skull 2. Segmentation • Partition in GM, WM, CSF

12. Registration of partitions Grey and white matter partitions are registered using a rigid body transformation, Simultaneously minimise sum of squared difference…

13. PET T1 MRI Between Modality Coregistration: II. Mutual Information

14. Co-registration in SPM

15. Co-registration in SPM Make selection Explains each option

16. Template: image that remains stationary Image that is ‘jiggled about’ to match template Defaults used by SPM for estimating the match, including Normalised Mutual Information Run Reslice options: choose from the menu for each of the three options (usually just defaults)

17. Spatial Normalisation

18. fMRI pre-processing sequence • Realignment • Motion correction: Adjust for movement between slices • Coregistration • Overlay structural and functional images: Link functional scans to anatomical scan • Normalisation • Warp images to fit to a standard template brain • Smoothing • To increase signal-to-noise ratio • Extras (optional) • Slice timing correction; unwarping

19. What is spatial normalisation? • Establishes a one-to-one correspondence between the brains of different individuals by matching each subject to a standard template • Allows: • Signal averaging across subjects • Determination of what happens generically over individuals • Identify commonalities and differences between groups (e.g. patients vs. healthy individuals) • Advantages: • Activation sites can be reported according to their Euclidian coordinates within a standard space (e.g. MNI or Tailarach & Tournoux, 1988) • Increases statistical power

21. Methods of registering images • Label-based • Identifies homologous features (points, lines and surfaces) in the image and template and finds the transformations that best superimpose them • Limitations:few identifiable features; features can be identified manually (time consuming & subjective) • Non-label based (aka intensity based) • Identifies a spatial transformation that optimizes some voxel-similarity between a source and image measure by: • Minimising the sum of squared differences between the object and template image • Maximising correlation coefficient between the images. • Limitation:susceptible to poor starting estimates

22. Spatial Normalisation in SPM • 2 steps involved in registering any pair of images: • Linear registration - 12-parameter affine transformation – accounts for major differences in head shape and position • Nonlinear registration – warping – accounts for smaller-scale anatomical differences

23. Priors/Constraints • Both linear and non-linear registrations use prior knowledge of the variability of the head and size to determine constraints • Priors/constraints are calculated using estimators such as the maximum a posteriori (MAP) or the minimum variance estimate (MVE)

24. Step 1 – Affine transformation (Linear) • Aim:to fit the source image f to a template image g, using a 12-parameter affine transformation • Performed automatically by minimizing squared distance between parameters and expected values • 12 parameters = 3 translations and 3 rotations (rigid-body) + 3 shears and 3 zooms • Accounts for overall shape, size, position and orientation rotation sheer translation zoom

25. Step 2 – Warping (non-linear) • Corrects gross differences in head shapes that cannot be accounted for by the affine transformation • Warps are modelled by linear combinations of smooth discrete cosine transform basis functions • Uses relatively small number of parameters (approx. 1000)

26. Non-linear basis functions Deformations are modelled with a linear combination of non-linear basis functions

27. Over-fitting • Regularisation – necessary so that nonlinear registration does not introduce unnecessary deformations • Ensures voxels stay close to their neighbours Affine registration (linear) Template Non-linear registration without regularisation Non-linear registration with regularisation

28. Limitations • Difficult to attempt exact structural matches between subjects, due to individual anatomical differences • Even if anatomical areas were exactly matched, it does not mean functionally homologous areas are matched too • This is particularly problematic in patient studies with lesioned brains • Solution: To correct gross differences followed by spatial smoothing of normalised images…

29. Normalisation in SPM Calculates warps needed to get from your selected images – saves in sn.mat file

30. Select the image that will be matched to the template • Select image(s) to be warped using the sn.mat calculated from the Source Image • Select SPM template • Select voxel sizes for warped output images

31. References • Ashburner & Friston – Spatial Normalisation Using Basis Functions, Chapter 3, Human Brain Function, 2nd Ed • Ashburner & Friston – Nonlinear Spatial Normalisation Using Basis Functions, Human Brain Mapping, 1999 • Ashburner & Friston - Multimodal image coregistration and partitioning--a unified framework, Neuroimage, 1997 • MFD slides from previous years • http://www.fil.ion.ucl.ac.uk/spm/course/slides08-zurich/