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Coregistration and Spatial Normalisation

Learn how co-registration and spatial normalisation techniques are used in fMRI studies to align images with a standard template, allowing for comparison across subjects and groups. Explore the principles, advantages, and methods of these processes, including affine registration and mutual information. Understand the importance of establishing a one-to-one correspondence between brains, enhancing statistical power, and identifying activation sites accurately. Discover how spatial normalisation in SPM involves linear and nonlinear registration steps with prior constraints to optimize image alignment. Gain insights into the benefits and limitations of label-based and non-label-based registration approaches in neuroimaging research.

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Coregistration and Spatial Normalisation

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  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

  20. 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

  21. 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

  22. 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)

  23. 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

  24. 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)

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

  26. 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

  27. 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…

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

  29. 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

  30. 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/

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