applywarp --in=core_mask_struct --out=core_mask_MNI --ref=MNI152_T1_2mm_brain --interp=nn --warp=my_nonlinear_transf
--in: filename of input image (to be warped) core_mask_struct
--out: filename for output (warped) image core_mask_MNI
--ref: filename for reference image MNI152_T1_2mm_brain
--interp: interpolation method {nn,trilinear,sinc,spline} nn
--warp: filename for warp/coefficient (volume) my_nonlinear_transf
Usage:
applywarp -i invol -o outvol -r refvol -w warpvol
applywarp -i invol -o outvol -r refvol -w coefvol
Compulsory arguments (You MUST set one or more of):
-i,--in filename of input image (to be warped)
-r,--ref filename for reference image
-o,--out filename for output (warped) image
Optional arguments (You may optionally specify one or more of):
-w,--warp filename for warp/coefficient (volume)
--abs treat warp field as absolute: x' = w(x)
--rel treat warp field as relative: x' = x + w(x)
-d,--datatype Force output data type [char short int float double].
-s,--super intermediary supersampling of output, default is off
--superlevel level of intermediary supersampling, a for 'automatic' or integer level. Default = 2
--premat filename for pre-transform (affine matrix)
--postmat filename for post-transform (affine matrix)
-m,--mask filename for mask image (in reference space)
--interp interpolation method {nn,trilinear,sinc,spline}
--paddingsize Extrapolates outside original volume by n voxels
-v,--verbose switch on diagnostic messages
-h,--help display this message
