Update ['pixdim'] after Spacing transform in meta dict.

monai/data/image_reader.py

@@ -1110,6 +1110,8 @@ def get_data(self, img) -> tuple[np.ndarray, dict]:
 
         for i, filename in zip(ensure_tuple(img), self.filenames):
             header = self._get_meta_dict(i)
+            if MetaKeys.PIXDIM in header:
+                header[MetaKeys.ORIGINAL_PIXDIM] = np.array(header[MetaKeys.PIXDIM], copy=True)
             header[MetaKeys.AFFINE] = self._get_affine(i)
             header[MetaKeys.ORIGINAL_AFFINE] = self._get_affine(i)
             header["as_closest_canonical"] = self.as_closest_canonical

monai/transforms/inverse.py

@@ -21,7 +21,7 @@
 from monai import transforms
 from monai.data.meta_obj import MetaObj, get_track_meta
 from monai.data.meta_tensor import MetaTensor
-from monai.data.utils import to_affine_nd
+from monai.data.utils import affine_to_spacing, to_affine_nd
 from monai.transforms.traits import InvertibleTrait
 from monai.transforms.transform import Transform
 from monai.utils import (
@@ -197,6 +197,9 @@ def track_transform_meta(
                 else:
                     raise
             out_obj.meta[MetaKeys.AFFINE] = convert_to_tensor(affine, device=torch.device("cpu"), dtype=torch.float64)
+            if MetaKeys.PIXDIM in out_obj.meta:
+                spacing = affine_to_spacing(out_obj.meta[MetaKeys.AFFINE])
+                out_obj.meta[MetaKeys.PIXDIM][1 : 1 + len(spacing)] = spacing
 
         if not (get_track_meta() and transform_info and transform_info.get(TraceKeys.TRACING)):
             if isinstance(data, Mapping):

monai/transforms/spatial/dictionary.py

@@ -24,11 +24,13 @@
 import numpy as np
 import torch
 
+import monai.transforms as transforms
 from monai.config import DtypeLike, KeysCollection, SequenceStr
 from monai.config.type_definitions import NdarrayOrTensor
 from monai.data.box_utils import BoxMode, StandardMode
 from monai.data.meta_obj import get_track_meta
 from monai.data.meta_tensor import MetaTensor
+from monai.data.utils import is_supported_format
 from monai.networks.layers.simplelayers import GaussianFilter
 from monai.transforms.croppad.array import CenterSpatialCrop
 from monai.transforms.inverse import InvertibleTransform
@@ -520,6 +522,11 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor], lazy: bool | None = No
                 output_spatial_shape=output_shape_k if should_match else None,
                 lazy=lazy_,
             )
+            if isinstance(d[key], MetaTensor) and f"{key}_meta_dict" in d:
+                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)
             if output_shape_k is None:
                 output_shape_k = d[key].peek_pending_shape() if isinstance(d[key], MetaTensor) else d[key].shape[1:]
         return d

monai/utils/enums.py

@@ -528,6 +528,8 @@ class MetaKeys(StrEnum):
     Typical keys for MetaObj.meta
     """
 
+    PIXDIM = "pixdim"  # MetaTensor.pixdim
+    ORIGINAL_PIXDIM = "original_pixdim"  # the pixdim after image loading before any data processing
     AFFINE = "affine"  # MetaTensor.affine
     ORIGINAL_AFFINE = "original_affine"  # the affine after image loading before any data processing
     SPATIAL_SHAPE = "spatial_shape"  # optional key for the length in each spatial dimension