Our clinical-grade DTI data have only 20 directions and a b-value of 1000, without reverse phase-encode collection. What is the best reconstruction workflow would you suggest for our dMRI data to do an automated fiber quantification? I searched this question with Google, and it suggested to use pyAFQ pipeline for these low-angular resolution data. Do you agree with this response?
At the same time, I also want to compute white matter free water measures along the segmented tracts. Do you think our data can fulfill this purpose?
QSIRecon does not have a single-shell workflow for free water estimation. Although by running the autotrack recon spec above, you will still get a GQI fit on your data which quantifies (among other things) isotropic diffusion.
If you need tract profiles, you would need to use a PyAFQ pipeline. But as far as I know, you can only get profiles for certain common scalars (e.g., DTI and DKI measures), not any arbitrary scalar map (e.g. your free water measures). So in your case, no out-of-the-box QSIRecon spec will do everything you want.
Sorry that I was distracted by other project tasks. I just fully checked our dMRI data which are a bit heterogeneous: majority of our dMRI data have 60 directions with a single b-value of 1000 (n=814), the second most of the data have 20 directions with a b-value of 1000 (n=498), and minority of the data have other number of directions (e.g. 32, 33 [single-shell] or 99 [multi-shell]) (total n=92).
If I want to group the data with 60 and 20 directions together for the statistical analysis, is it more appropriate to use the same reconstruction workflow? Or should I use a better optimized workflow for the data with 60 directions, and don’t group the heterogeneous data together? If yes, can you recommend a workflow for the data with 60 directions?