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Dear experts, I am running FEAT which includes unwarping of the functional images with a GRE fieldmap. I have prepared the fieldmap images as instructed in the documentation. After examination of the resultant FEAT output and temporary files, I noticed that the unwarping and co-registration steps are introducing "grid-like" artifacts to the top and bottom slices of the functional runs, and is consistent across subjects. Inspection of the report logs (and with fsleyes) reveal is has corrected (to some degree) the susceptibility inhomogeneities. Here is the example_func image: https://www.dropbox.com/s/jwc0t834nfk3w70/example_func.nii.gz?dl=0 And the example example_func_distorted: https://www.dropbox.com/s/hse5el8xbr8p45l/example_func_distorted.nii.gz?dl=0 --- As you will probably note this is an older acquisition where the voxel resolution in the sagittal plane (6mm) is larger than typical sequences (multiband acceleration was not available), leading to incomplete brain coverage across most subjects. The issue appears to occur between the calling of the epi_reg and applywarp functions within preprocessing stage 1, where the example_func_undistorted image is created. Manually running epi_reg outside of FEAT did not show these artifacts. Contrary to some older posts (https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=fsl;fc64216.1411), the applywarp commands proceeding straight after mcflirt also did not introduce these artifacts into the filtered_func images etc. For example, see: https://www.dropbox.com/s/4eiprhdy6qvw6mk/filtered_func_data.nii.gz?dl=0 So we've exhausted a few options, and wondering given that the prefiltered_func_data and resultant filtered_func_data do not contain these artifiacts, if further analysis steps are going to be contaminated? We are now running ICA to see if these noise components will be present. We have also ensured conservative brain masks of the magnitude images, have checked the field map orientations are the same as per the functional runs, demeaned the fieldmaps, and checked the results from the field map preparation etc. The larger voxel resolution and incomplete brain coverage will evidently lead to these rather noisy voxels at the top and bottom slices. But we would prefer not to remove such voxels from further analysis, for obvious reasons. We have nonetheless run the same FEAT parameters on high-resolution data (from the same scanner), and are still experiencing this gridding at the top/bottom (particularly) slices - but anyway for some reason the distortion correction hasn't worked that well: https://www.dropbox.com/s/l0h1stxroi9bw7a/highres_example_func.nii.gz?dl=0 All the available logs are found here: https://www.dropbox.com/sh/x1deao2qo8p96d4/AAAH-oODf6PWveCLqXtKPuhMa?dl=0 Alistair ######################################################################## To unsubscribe from the FSL list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=FSL&A=1