Hello MRtrix experts,
I met a problem when I co-register “5tt-image” to DWI using a patient data acquired on Siemens Prisma 3T MRI system . I ran the command given in the "BATMAN-tutorial-update2020.
This indeed doesn’t look ideal. It seems there might be some scaling and/or shearing going on there. Are you sure you provided the -dof 6 option to the flirt command…?
Otherwise, can you post the copy/paste of the exact commands you used? I assume you modified the command from the BATMAN tutorial (if nothing else, to replace the long dashes – with hyphens -), we’d need to see the exact procedure you used here.
It’s according to old version tutorial , and occurred a warning: An input intended to be a single 3D volume has multiple timepoints. Input will be truncated to first volume, but this functionality is deprecated and will be removed in a future release. when run flirt -in mean_b0_preprocessed.nii.gz -ref 5tt_nocoreg.nii.gz -interp nearestneighbour -dof 6 -omat diff2struct_fsl.mat. The result is as following,
Have you tried using a skull-stripped version of the T1-weighted image?
The trouble with using the full T1w is that the ideal alignment between the two images is merely a tiny valley among a huge multi-dimensional space where the two images can be made to “overlap” as far as the complex registration similarity metric is concerned. Sometimes imagining yourself being the registration similarity metric can assist in understanding why some things work and others don’t.
I would also take a look at your prior DWI pre-processing. There are some slightly funny-looking brain shapes going on, so there’s a small chance that EPI distortion correction may have gone awry, e.g. increasing rather than decreasing spatial distortions. This would make inter-modal rigid-body registration harder, because the ideal alignment would not stand out so much from non-ideal alignment.
I would also look at the actual 5tt_nocoreg.mif image itself. It looks like it might have been cropped in the frontal and cerebellar regions. Maybe the 5ttgen step didn’t work as expected? If the raw segmentations are not correct, then no amount of registration is going to help here…
Another thing you could try is to add the -stride 1,2,3 option to both mrconvert calls in your first situation (the raw T1). There’s a chance that could be throwing flirt off (not an issue with your last case though, I can see that’s been dealt with).
Ok, there’s a good chance the segmentation might fail in the presence of tumours… I’d recommend you take a look at the 5tt image overlaid on the T1 it was derived from, and check that everything matches expectation.
If the 5ttgen fsl internal brain extraction does not work well, you can perform this extraction yourself using any method that works, and then run 5ttgen fsl using one of the -mask or -premasked options.