I am currently working on a project with the HCP dataset.
We are combining fixel metrics (transformed into voxel based metrics with fixel2voxel) with voxel-based DTI and structural (T1/T2) metrics in a multi-parametric analysis. Therefore, we want to ensure all these maps are in the same space and that the alignment is optimal in white and grey matter.
When using the FODs (resolution: 1.25mm) only to drive registrations and bringing everything into that FOD template space, we found the alignment in grey matter was rather poor (visible in warped T1 and T2 maps). To improve registrations, I tried:
- increasing the number of non-linear steps, adding 4 x 1.0 to nl_scale, 4 x 8 to nl_lmax and 4 x 25 to nl_niter when generating the template with population_template. I did not see any difference.
- using multiple contrast (FOD, T1, T2) to drive the registration in population_template, creating 3 templates (done with default parameters since modifying these did not seem to have any impact on registration) and then using these templates to compute transforms with mrregister. We were still not satisfied with the registration and thought this may be because by downsampling the T1 and T2 from 0.7 to 1.25mm (to match the FOD resolution) we were not really improving the accuracy of information in grey matter, which led us to try this:
- specifying a -voxel_size 0.7 in population_template. I ran a test with only the FODs and one with multiple contrasts (FOD, T1 and T2) with the voxel_size option at 0.7. It looks like using this resolution improved the registrations, but I am still unsure if using the T1 and T2 as contrasts when generating templates improves the registration.
I was wondering if using a lower resolution than the resolution of my FOD is appropriate. In your documentation, upsampling the DWI data is suggested to improve template building and registration, but here we upsampled the FODs. If this can improve our registrations, we could go back and upsample our DWI images to 0.7 before carrying on with the next steps. I look forward to hear your thoughts on this.
Lastly, I was wondering if there is a specific reason why mrregister and mrtransform are used in the fixel-based analysis steps instead of computing the warps and applying them to the images in one step with the population_template function (using the -warp_dir and -transformed_dir options)? Or am I misunderstanding the use of these option?