Update ['pixdim'] after Spacing transform in meta dict. #8269
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According to the issue, this PR addresses on the meta dictionary `data['pixdim']` of NIfTI images does not update after applying the `spacing` or `spacingd`.
To align with `affine`, we update `data['pixdim']` and keep the original metainfo in `data['original_pixdim']`.
Additionally, this PR also update the metainfo `key_{meta_key_postfix}['pixdim']` in NIfTI images, consistent with `spaced_data_dict['image_meta_dict']['pixdim']` in issue Project-MONAI#6832.
Signed-off-by: Wei_Chuan, Chiang <[email protected]>
Co-authored-by: einsyang723 <[email protected]>
Co-authored-by: IamTingTing <[email protected]>
slicepaste
changed the title
Signed-off-by: Wei_Chuan, Chiang <[email protected]>
Signed-off-by: Wei_Chuan, Chiang <[email protected]>
Signed-off-by: Wei_Chuan, Chiang <[email protected]>
Signed-off-by: Wei_Chuan, Chiang <[email protected]>
Signed-off-by: Wei_Chuan, Chiang <[email protected]>
| @@ -477,6 +477,10 @@ def pixdim(self): | |||
| return [affine_to_spacing(a) for a in self.affine] | |||
| return affine_to_spacing(self.affine) | |||
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| def set_pixdim(self) -> None: | |||
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Does it make sense for this to be a method? If it's only going to be called in one place it's simple code could just be put there. If there's anticipation that this would be called by other things then that's fine.
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Thanks for your quick reply.
Originally, we considered other files such as DICOM might use Spacing, which could involve the usage and access meta_tensor.py property. Therefore, we decided to define a method.
However, after we reevaluating the entire codebase this week, it might be better to modify data["pixdim"] directly within TraceableTransform instead.
| @@ -535,6 +535,9 @@ def __call__( | |||
| dtype=dtype, | |||
| lazy=lazy_, | |||
| ) | |||
| if isinstance(data_array, MetaTensor) and "pixdim" in data_array.meta: | |||
| data_array = cast(MetaTensor, data_array.clone()) | |||
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Why is it necessary to clone the data array here? This is going to have a cost and I think isn't compatible with lazy resampling. Perhaps this is code that should be SpactialResample instead? @atbenmurray If you could please check if this is going to interact with laziness, thanks.
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@ericspod Thank you for your suggestion.
We initially considered using data_array.clone() based on the following issue:
# LoadImage
data = {'image', 'data1.nii'}
imgloader = LoadImaged(keys=('image'), image_only=False, ensure_channel_first=True)
input_data_dict = imgloader(data)
# Spacing
respacing = transforms.Spacingd(keys=['image', 'label'], pixdim=(1, 1, 10), mode=('bilinear'))
spaced_data_dict = respacing(input_data_dict)
Originally, if we didn't use data_array.clone() and directly modified the data, the MetaTensor in both input_data_dict and spaced_data_dict would be affected simultaneously.
This means input_data_dict would lose its original input_data_dict["pixdim"] information.
However, as suggested above, using .clone() in this way is not ideal as it introduces additional costs.
If we perform this modification within TraceableTransform.track_transform_meta() method, which is executed by SpatialResample, it might no longer be a concern.
| if "filename_or_obj" in d[key].meta and is_supported_format( | ||
| d[key].meta["filename_or_obj"], ["nii", "nii.gz"] | ||
| ): | ||
| d = transforms.sync_meta_info(key, d) |
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May I ask why we need this sync here seems it already been done in the MapTransform?
MONAI/monai/transforms/transform.py
Line 426 in 996e876
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Thank you for your question. In MapTransform, it only synchronizes MetaTensor, but since {key}_meta_dict is not a MetaTensor format, it won't be updated automatically. That's why we added this line of code to ensure the information in "{key}_meta_dict gets synchronized as well.
| @@ -535,6 +535,9 @@ def __call__( | |||
| dtype=dtype, | |||
| lazy=lazy_, | |||
| ) | |||
| if isinstance(data_array, MetaTensor) and "pixdim" in data_array.meta: | |||
| data_array = cast(MetaTensor, data_array.clone()) | |||
| data_array.set_pixdim() | |||
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It seems that the pixel dimensions (pixdim) are only updated in the Spacing transformation. This is why I previously suggested that we should only retain the original_pixdim, as the latest pixdim can be derived from the metadata in the MetaTensor and the affine transformation.
Additionally, the use of meta_dict for logging metadata is becoming outdated. Perhaps we no longer need to maintain it? What are your thoughts on this?
import torch
from monai.transforms import Spacing
data = torch.randn(2, 1, 32, 32, 32)
trans = Spacing(pixdim=(1.5, 1.5, 1.0))
out = trans(data)
print(out.pixdim) -- > (1.5, 1.5, 1.0)
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@KumoLiu Thank you for your detailed feedback.
Our original thinking was that all related information should be update, including:
data['image']['pixdim']
data['image'].pixdim
data['image_meta_dict']['pixdim']
However, after reading your response, we're a bit uncertain: are you suggesting that we don't need to update pixdim in {key}_meta_dict?
Or will there be a new way to handle {key}_meta_dict in future releases?
If possible, could you please elaborate on this? Thank you.
Fixes #6840
Description
According to the issue, this PR addresses on the meta dictionary
data['pixdim']of NIfTI images does not update after applying thespacingorspacingd. To align withaffine, we updatedata['pixdim']and keep the original metainfo indata['original_pixdim']. Additionally, this PR also update the metainfokey_{meta_key_postfix}['pixdim']in NIfTI images, consistent withspaced_data_dict['image_meta_dict']['pixdim']in issue #6832.Types of changes
./runtests.sh -f -u --net --coverage../runtests.sh --quick --unittests --disttests.make htmlcommand in thedocs/folder.