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Hi Sandra, What bet -f and -g options have you tried? -f 0.2 worked for me but it depends on the dataset. When bet struggled with magnitude images in my last project, I masked out the non-brain tissue. ###I used fsl_anat because it gave a good brain-extracted structural image - it was a bit "tight" that is better for the fieldmap than to have lots of non-brain tissue: fsl_anat -i struc.nii.gz -o strucfiles ###Then a transform was created between the structural and functional image space: flirt -in mag_EPI.nii.gz -ref strucfiles.anat/T1_biascorr.nii.gz -out mag_EPI2STRUC.nii.gz -omat mag_EPI2STRUC.mat -bins 256 -cost corratio -searchrx -30 30 -searchry -30 30 -searchrz -30 30 -dof 6 -interp trilinear -paddingsize 0 ###The transform was inverted: convert_xfm -omat mag_STRUC2EPI.mat -inverse mag_EPI2STRUC.mat ###And the brain-extracted structural image was registered to the functional image space: flirt -in strucfiles.anat/T1_biascorr_brain.nii.gz -applyxfm -init mag_STRUC2EPI.mat -out mag_EPI_MASK.nii.gz -paddingsize 0 -interp trilinear -ref mag_EPI.nii.gz fslmaths mag_EPI_MASK.nii.gz -bin mag_EPI_MASK.nii.gz ###If it is too large, you may add the -ero option: fslmaths mag_EPI_MASK.nii.gz -ero mag_EPI_MASKero.nii.gz ###This will give you a mask in the same space as the magnitude file that can be used to mask-out non-brain tissue. fslmaths mag_EPI.nii.gz -mas mag_EPI_MASK.nii.gz mag_EPI_brain.nii.gz ### And you can used to prepare fieldmap fsl_prepare_fieldmap <scanner option> pha_EPI.nii.gz mag_EPI_brain.nii.gz fmap_EPI.nii.gz <time diff.> You will have to put it in bash and run for each subject. I hope this helps. Karolina