Dear MRtrix experts,
I am currently analyzing my DWI data of a group of brain tumor patients, on which I would like to do whole-brain tractography to generate structural connectomes. However, I’m not entirely sure about the optimal way to prepare my data for this purpose.
In particular, I have 2 separate datasets. The first one (acquired by myself) is a multi-shell DWI protocol, including PA scan for optimal distortion corrections. Using this sequence, I have data for 11 control subjects, 13 meningioma brain tumors (pushing the brain aside, but brain remains intact) and 10 glioma brain tumors (infiltrating -“eating away”- the brain). Then, I have another dataset of 5 glioma patients, with single-shell acquisition without PA scan.
In my preprocessing (using the latest MRtrix version 3.15) I included the following:
- distortion correction using eddy & topup + bias field correction using fast
- coregistration of T1 to DWI space
- 5tt segmentation of coregistered T1
- intensity normalization DWI images (using dwinormalise)
- estimating msmt response function per subject
My first question is whether or not to average my response function across subjects. If I should, should I then average across both studies (multi + single shell) or within study? I guess averaging across studies wouldn’t make much sense, but on the other hand if I average per study, I assume my response function will be biased because in one study I include controls + patients, whereas in the other study I only have 5 patients.
Secondly, in the documentation it says that before you average your response function, you should do inter-subject intensity normalisation. Is this step accomplished using the dwinormalise command?
In case averaging wouldn’t be appropriate, I found on the documentation that the simplest and most common solution is to use an identical number of streamlines for every subject in connectome construction. This is also what I tried in the first place: generating 10M tracts and “sift”-ing to 1M. However, some of the glioma patients have very large lesions - some with almost an entire lobe affected/resected. For these patients, you would suspect a priori to find less tracts. So I’m looking for a better termination criterion for tract generation and sifting as well.
All suggestions and advice is welcome!