External Email - Use Caution
I've really impressed with mri_easy_reg and I was hoping to use it in a similar way as ANTs built_template scripts, which requires separation of affine from nonlinear transforms for their unbiased template updating algorithm. However, mri_easy_reg only returns a single transform object, combining affine and nonlinear components.
I added an argument as an option for producing separate transforms
``` def main(): parser.add_argument("--separate_transforms", default=None, help="(optional) Produce Affine and NonLinear Fields Separately, if 'a' produce combined transform too. If 'b', do not produce combined transform") ```
then include ``` if args.separate_transforms is not None: sepAII3 = II2 sepAJJ3 = JJ2 sepAKK3 = KK2 sepAaffine = torch.tensor(np.matmul(Mflo, atlas_aff), device='cpu') sepARAS_X = affine[0, 0] * sepAII3 + affine[0, 1] * sepAJJ3 + affine[0, 2] * sepAKK3 + affine[0, 3] sepARAS_Y = affine[1, 0] * sepAII3 + affine[1, 1] * sepAJJ3 + affine[1, 2] * sepAKK3 + affine[1, 3] sepARAS_Z = affine[2, 0] * sepAII3 + affine[2, 1] * sepAJJ3 + affine[2, 2] * sepAKK3 + affine[2, 3] ``` and ``` if args.separate_transforms is not None: #create separate nonlinear naked field ndepth, nheight, nwidth, _ = f2r_field.shape # Create meshgrid indices for each dimension nII, nJJ, nKK = np.meshgrid(np.arange(ndepth), np.arange(nheight), np.arange(nwidth), indexing='ij') # Convert numpy arrays to PyTorch tensors nII2 = torch.tensor(nII, dtype=torch.float32, device='cpu') # or device='cuda' if using GPU nJJ2 = torch.tensor(nJJ, dtype=torch.float32, device='cpu') nKK2 = torch.tensor(nKK, dtype=torch.float32, device='cpu') # Now use these tensors as inputs to your interpolation function nFIELD = fast_3D_interp_field_torch(f2r_field, nII2, nJJ2, nKK2) nII3 = nII2 + nFIELD[:, :, :, 0] nJJ3 = nJJ2 + nFIELD[:, :, :, 1] nKK3 = nKK2 + nFIELD[:, :, :, 2] ``` ``` and saves them if args.separate_transforms is not None: print("args.separate_transforms is not None") # Correctly combine the prefix with the filename fwd_field_dir = os.path.dirname(args.fwd_field) fwd_field_basename = os.path.basename(args.fwd_field) nonlinear_fwd_field = os.path.join(fwd_field_dir, nonlinear_prefix + fwd_field_basename) print(f" Saving nonlinear field to: {nonlinear_fwd_field}") try: nonlinear_field = torch.stack([nII3, nJJ3, nKK3], axis=-1) print(f" Nonlinear field shape: {nonlinear_field.shape}") save_volume(nonlinear_field, Raff, Rh, nonlinear_fwd_field, n_dims=3) print(" Nonlinear field saved successfully") except Exception as e: print(f" Error saving nonlinear field: {str(e)}")
# Do the same for the affine field affine_fwd_field = os.path.join(fwd_field_dir, affine_prefix + fwd_field_basename) print(f" Saving affine field to: {affine_fwd_field}") try: save_volume(torch.stack([sepARAS_X, sepARAS_Y, sepARAS_Z], axis=-1), Raff, Rh, affine_fwd_field, n_dims=3) print(" Affine field saved successfully") except Exception as e: print(f" Error saving affine field: {str(e)}") ``` full python file attached.
but it doesn't work. When I try applying them to floating image one at a time, the images go haywire. On the left is after applying the affine transform my code generated, on the right is applying the nonlinear transform onto the image on the left.
[cid:2ae2ee64-8c83-49f5-b455-3348a9ec5983]
I know this is an unusual request, but thanks for any help Josh
Josh Lee
Assistant Professional Researcher
Department of Psychiatry and Behavioral Sciences
University of California, MIND Institute
jkilee@ucdavis.edu
