Hi John,
Thank you very much for the quick response. It's greatly appreciated.
I have been having problems with partial volume effects in a longitudinal VBM analysis of Huntington's disease patients. When I compare two timepoints (~ 2months apart) across all my participants I get a large periventricular band. At ~2months, I am not expecting to see any changes due to disease progression.
A colleague from the FIL, suggested that instead of segmenting the average map (output from the longitudinal registration), I should instead segment each timepoint separately and then create an average from that, which I could use for dartel.
The meanrc* images (created by averaging the separate rc* images) look a bit cleaner than the rc_avg* (created by segmenting the average T1w volume), with lower GM probability assigned to periventricular voxels. However, for the meanrc* images the qform is the same as the sform matrix (since they are just an average). Normalisation of the c1 images at a later stage is not correct.
From your reply, I understand that each rc*image (for each timepoint), will have a different qform matrix. Is it correct to simply copy the qform matrix from each rc* image to the u_meanrc* Template when normalizing each timepoint?
If this is not a correct approach, is there anything that you can suggest to improve segmentation results of the average images? I have previously read about masking the c1* images with the c3* images (CSF), but I was hoping there would be a better option available.
Many thanks again for the help,
Marina
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