Hi Peter,
Aha, that explains a lot. tckglobal relies on the multi-tissue model for multi-shell data, which can easily become unstable when fitting 3 tissues (WM/GM/CSF) to 2 shells (b=0 and b=X). If you want to work with this data, rather than a HCP-style phantom, I would recommend using a 2-tissue WM/CSF-model within a WM mask.
That will be fine then. I was just wondering if you might have used the ground-truth response functions that you used for simulating the phantom. Yes, if the scaling would have been wrong you could correct this with the weight parameter, but then you would also need to modify ppot because its default (5% * w) assumes equalised scaling.
When #tissues > #shells, the tissue volume fractions are not uniquely defined without additional priors, which impedes direct MT-CSD of single-shell data. Global tractography provides a spatial prior that might help, and I’ve had limited success with single-shell data of b>2000. But low-b single shell data is very challenging for our multi-tissue model because the DWI intensity in GM is still high at low b. Therefore, I’d recommend against it…
Again, it seems like this is mute now. All I meant was that the particle length defaults to 1mm, regardless of the voxel size encoded in the image header. Hence, if the voxel size is not in the expected range of 1-3mm (e.g. in small animal data or phantoms), the particle length may need to be set differently.
Good to know, it’s a relief…
For human brain data, I’ve never seen large improvement beyond 1e9 iterations, so that should be fine.
Cheers,
Daan