If I understand correctly then you have images whose spatial extent are (a)
9 x 6 x 1.2 cm^3 and (b)
1.6 x 0.7 x 0.6 cm^3? Those are quite small and quite different. I can imagine that if image 2 covers a very specific sub-region of image 1 (because it is much smaller) you may need to give a good starting estimate to get a correct result.
if input_image
has the dimensions (b)
and good_size_image
has the dimensions (a)
then with the call
flirt -in input_image
-ref good_size_image
-out output_image
-applyxfm
(init
option is not strictly necessary in this case),
your output will be the image showing the much smaller space in much larger voxels. I assume the output image would have the size of the reference image (that is the idea of the reference image) but most of it would be empty. Switching (a)
and (b)
would not make sense because the space of the reference image can then only cover a tiny part of the input. You would need to register first.
You may want to experiment with the option applyisoxfm
which resamples an image to cubic voxels of a given size.
flirt -in small_image -ref small_image -out small_1mm -applyisoxfm 1
will resample the image that covers the space (b) to 1x1x1 mm^3. Again, that is probably to coarse a resolution for so small a space. maybe resample both images to 0.5 mm isotropic and then register?
For these problems the FSL mailing list is a much better
place to find help.
tri-linear interpolation
see this inverse interpolation of multidimensional grids it might help a bit (the image there is tri linear filtering) but I suggest you google out tri-linear interpolation (Wiki) – Echinate