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Hi Tamir,
I am assuming all registrations are “good” ( that is none of these differences are due to a failed registration ). For any transformation between images which isn’t equivalent to integer scaling between two perfectly aligned grids, there will be voxels which only have a partial overlap. For a binary mask, this will result in voxels in the transformed image having values in the range 0..1, this will almost certainly alter quantities such as non-zero volume of the mask. We usually partially account for this by thresholding-and-binarising the transformed mask at some value - this is typically 0.5, but the choice is somewhat arbitrary.

Note that even a native-space "ground-truth” mask shouldn’t be expected to be same size for all subjects - some of the mask-size variation will be due to true subject anatomical variability.

Hope this helps,
Kind Regards
Matthew
--------------------------------
Dr Matthew Webster
FMRIB Centre 
John Radcliffe Hospital
University of Oxford

On 1 Aug 2022, at 10:15, Tamir Eisenstein <[log in to unmask]> wrote:

Hi FSL experts,

I have three runs per subject acquired at the same session - two resting state and a task, which have the same spatial and temporal resolutions.

I created a mask from a cluster of activation from the task group-level analysis and wanted to transform it from standard to native space for each of the three runs in each subject.

However, for each run in the same subject, I get a slightly different transformed mask with different number of voxels.

In addition, for different subjects I get significantly different masks in terms of voxels number - isn't a specific MNI mask transformation to different native spaces suppose to yield the same native-space mask in terms of voxels number across all subjects?

I have followed the instructions on the web to conduct the invert transformation (with my own paths and file names of course):

invwarp -w highres2standard_warp -o standard2highres_warp -r highres

applywarp -i activationMaskMNI -r example_func -o activationMaskNative -w standard2highres_warp --postmat=highres2example_func.mat

I would very appreciate any insights,
Many thanks,
Tamir

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