Hi Anastasia!
I will go backwards with increasing difficulty for me to understand everything:
4. I see, yes this would not make sense. Thank you for the explanation!
3. With fslview I will have to run through 393,120 slices, then. (72 slices in z-direction for our 70 DWI scans for tp1 and tp2 for each of our 39 subjects) I will go dwi image by dwi image running through the 72 slices rather fast. Just to make sure I am not wasting a lot of time: is this what I should do? If I detect slices that are much darker than their neighbors, will I need to exclude this subject for the analysis or can I do something about it?
2. Yes, I use bbregister and I also use the anatomical T1 weighted scan to extract the brain mask (usemaskanat=1). To make sure that everything is alright, I would go slice by slice in tkmedit with tkmedit [subject] brainmask.mgz -surfs -aparc+aseg where [subject] would be the base AND both longitudinal runs. I understand that I need to check if white matter is where it should be, if the cortical and pial surfaces are where they should be and if the labeling is correct. Again, as this will take probably even longer than 3. I would like to make sure this is the right thing to do before starting the quality check.
Thanks again! Vincent
Hi Vincent - I'll take on the tracula-related parts:
- For tracula, the part of the recon-all output that matters is the
aparc+aseg. The surfaces will play a role only the DWI-to-T1 registration (assuming you opt to use bbregister).
- It's important to check your DWI data for obvious motion artifacts,
(slices that are much darker than their neighbors). Right now this has to be done visually, but it's on my list to produce some motion metrics as part of the preprocessing.
- The ball-and-stick model (that bedpostx fits to your data) is used by
the tractography algorithm in tracula, but there are no stats produced on the parameters of that model currently. That's something that can be added in the future as well. Note though that it wouldn't make sense to just average f1 or f2 over the pathway, because compartment 1 in one voxel may correspond to compartment 2 in some other voxel.
Hope this helps, a.y
On Fri, 11 Oct 2013, vbrunsch@nmr.mgh.harvard.edu wrote:
Dear Freesurfer experts,
I want to do a quality check on our imaging data. I used the longitudinal stream for SBA and as the first step for the longitudinal white matter analysis with TRACULA. We had two time points in our study and thus, in the freesurfer output directory there are 5 folders per subject (2 cross-sectional runs, 2 long runs and the base).
- Would you recommend to use (all of) the QA_TOOLS on all of these 5
folders per subject for the SBA? 2. Independent of the previous question, for the longitudinal version of TRACULA would you recommend to use (all of) the QA_TOOLS on the freesurfer base folder only / additional folders? 3. In addition to the late visual check for well reconstructed pathways with freeview, is there another automated possibility to check the quality of the diffusion weighted images beforehand/do you think this is necessary?
- On another note: If I understand correctly, in TRACULA bedpostX is
used to reconstruct the pathways but then the mean over the voxels that were hit (by the MCMC sampling of the paths) of measures from the tensor model are taken as outputs. I wonder, is there also the possibility of using the partial volumes f1, f2,.. as output measures?
Best, Vincent _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer