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.

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