Alignment
- To switch between the individual subject and the template during alignment, use the space bar to toggle back and forth.
- Suppose you want to align relatively distorted EPI data to anatomy, and you know that the EPI data will not have a lot of intrinsic contrast (i.e., no iron oxide agent in use, so you have low-contrast BOLD data). In this case, acquire a relatively quick undistorted anatomical scan (e.g., T1 or fast-spin-echo T2) using a matched set of slices, then use this anatomical volume for a first alignment step. Then, because the EPI slice-selection step is the same as for the anatomical, remove the z and size-z parameters from the affine adjustment set, and align the EPI data using the first alignment as the starting set.
- Even though mutual information is designed to enable registration across contrasts and/or modalities, it is always better to use similar contrasts when possible (e.g., T2-weighted for both subject and template). If you are constantly registering across contrasts, consider replacing the template with the multi-subject average from your own dataset.
- Which is more important in terms of CPU size: the size of the the source/individual volume or the target/template volume? The answer is that CPU depends more strongly on the size of the template volume. A loop is performed over all voxels in the template space for each iteration, and then the transformation identities a point in the source and interpolates nearest neighbors. So, don't use excessive padding (larger FOV) on templates, but do include just a bit of space around the brain.
Other
- Provide suggestions. This section will be a work in progress.