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MRI white matter segmentation
Voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data. (Ashburner, 2000)
Strengths
- Fully automated & quick
- Investigates whole brain
Problems [Bookstein 2001, Davatzikos 2004, Jones 2005]
- Alignment difficult; smallest systematic shifts between groups can be incorrectly interpreted as FA change
- Needs smoothing to help with registration problems
- No objective way to choose smoothing extent
Solve alignment using alignment-invariant features, a skeleton.
- Use medium-DoF nonlinear reg to pre-align all subjects’ FA Register FA images together to create average FA image. (nonlinear reg: FNIRT)
- Create mean FA image (no smoothing)
- “Skeletonise” Mean FA
- Threshold Mean FA Skeleton. giving “objective” tract map
- For each subject’s warped FA, fill each point on the mean-space skeleton with nearest maximum FA value (i.e., from the centre of the subject’s nearby tract)
- Do cross-subject voxelwise stats on skeleton-projected FA
- Threshold, (e.g., permutation testing, including multiple comparison correction)