Usage do AffineTransform

[a-parida12]

I use the SpacingD transforms after LoadImageD and AddChannelD. I read in a nifti file of shape [368, 336, 368] , the input to the spacingD [1, 368, 336, 368]. The outputs of spacingD is a matrix with zeros.

I followed the trace and the last time I see non-zeros matrix is at

https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/layers/spatial_transforms.py#L575

The generated grid has a range [1,3] but the torch.grid_sample expects range [-1, 1].
More details here-
https://pytorch.org/docs/stable/generated/torch.nn.functional.affine_grid.html
https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html

I tried it various nifti files and gives me the same trouble. When I change the range to the expected range by substracting 2. i get a output, which looks good.

Can anyone verify the issue. I am using MonAi 1.0.0

[wyli]

if you share one of the nifti files or the header info, I can look into this.

[a-parida12]

I am not entirely sure. I think has to be dim[0] == 3 and not pixdim[0]. Also here is an example header from the internet https://dartbrains.org/content/Introduction_to_Neuroimaging_Data.html#loading-data-with-nibabel .

[wyli]

right, the header should be fine, then the isuse might be AddChanneld which is deprecated. Have you tried EnsureChannelFirstD? the following example works fine:

import numpy as np
import nibabel as nib
import monai.transforms as mt

img = np.ones((368, 336, 368)).astype(np.float32)
a = np.asarray([[0.41, 0.005, 0.008, -79.7], [-0.0049, 0.592, 0.0664, -57.4], [-0.0073, -0.0972, 0.404, -32.1], [0., 0., 0., 1.0]])
nib_img = nib.Nifti1Image(img, affine=a)
nib.save(nib_img, "test.nii.gz")
print(nib_img)

x = mt.LoadImageD("image")({"image": "test.nii.gz"})
x = mt.EnsureChannelFirstD("image")(x)
x = mt.SpacingD("image", (1.0, 1.0, 1.0))(x)
print(x["image"])
print(x["image"].shape)
print(x["image"].pixdim)

[a-parida12]

For me both the EnsureChannelFirstD and the AddChannelD give the same output. Here is a small snippet below-

import monai.transforms as mt
import numpy as np

x = mt.LoadImageD("image")({"image": "/home/abhijeet/Datasets/123.nii.gz"})
x = mt.EnsureChannelFirstD("image")(x)
print(np.min(x["image"]), np.max(x["image"]))
# 0.0 2740.2239
x = mt.SpacingD("image", (1.0, 1.0, 1.0))(x)
print(np.min(x["image"]), np.max(x["image"]))
# 0.0 4.2632482e-06

I would be happy to send the file to you if it helps. Let me know I can send it to you it is 160MB file.

[wyli]

thanks, I looked into this, probably it's a bug from computing the offset, I'll submit a PR to revise it.

[a-parida12]

Thank you @wyli !

[a-parida12]

@wyli I have similar troubles of the matrix becoming zeros after a rotation operation using RandRotateD is it also related to the issue you looked into before with the offset.

Also do you have a release or a nightly build that I could use with the changes

[wyli]

sure, if you are using the docker image the latest tag has the patch https://hub.docker.com/r/projectmonai/monai/tags. otherwise you can pip install with the git+ syntax https://docs.monai.io/en/latest/installation.html#from-github

[a-parida12]

Thanks again!!