Dear Mark,

Just to make sure we’re on the same page: I’ll say that two images are in the same voxel space when they have the same size (in voxels, e.g., 256x256x256) and the same vox2ras matrix - the one that maps voxel coordinates to RAS (physical) coordinates.

The subfield segmentation is indeed in the same RAS coordinates as your native T1, but not in the same voxel space.

One option that you have is to convert your subfield mask to the voxel space of the T1, with the option –rl (“reslice like”) of mri_convert:

For probabilistic masks:

mri_convert  subfield_prob.mgz  subfield_prob_T1voxelSpace.mgz -rl norm.mgz -odt float

For discrete masks or segmentations, you can use a similar command, but with nearest neighbor interpolation:

mri_convert subfield_mask.mgz subfield_mask_T1voxelSpace.mgz -rl norm.mgz -odt float -rt nearest

I hope this helps!

Cheers,

/Eugenio

 

 

 

 

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Hi.  I am just getting my feet wet with running my subjects through the Freesurfer processing stream, so the following question night be a bit trivial (I hope):

 

I have run qvlSegmentHippocampal subfields on one of my subjects, and it completed just fine with what seem to be the appropriate outputs in the subject’s mri folder.  I have another 100 subjects to run, but want to make sure I can accomplish what I would like to on this subject first.  The images which I see are sub-images, i.e. these have fewer voxels compared to the original image, but in the native T1 space resolution (= 1 mm3).  I would now like to register the subfields in the native space (my DTI/NODDI maps are already registered into this space), to obtain mean values within each subfield.  How can I transform these sub-field images back into the native T1 space, or is there a method within Freesurfer to perform the math (e.g. similar to fslmaths)?

 

Thanks,

 

Mark

 

____________________
Mark Wagshul, PhD
Associate Professor
Gruss Magnetic Resonance Research Center
Albert Einstein College of Medicine
Bronx, NY 10461

Ph: 718-430-4011

FAX: 718-430-3399

Email: mark.wagshul@einstein.yu.edu

 

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