diff --git a/monai/handlers/transform_inverter.py b/monai/handlers/transform_inverter.py
index 783a2b8570..a8d5a509df 100644
--- a/monai/handlers/transform_inverter.py
+++ b/monai/handlers/transform_inverter.py
@@ -10,6 +10,7 @@
 # limitations under the License.
 
 import warnings
+from copy import deepcopy
 from typing import TYPE_CHECKING, Callable, Optional, Sequence, Union
 
 from torch.utils.data import DataLoader as TorchDataLoader
@@ -31,7 +32,7 @@ class TransformInverter:
     """
     Ignite handler to automatically invert `transforms`.
     It takes `engine.state.output` as the input data and uses the transforms information from `engine.state.batch`.
-    The outputs are stored in `engine.state.output` with the `output_keys`.
+    The outputs are stored in `engine.state.output` with key: "{output_key}_{postfix}".
     """
 
     def __init__(
@@ -42,7 +43,7 @@ def __init__(
         batch_keys: Union[str, Sequence[str]] = CommonKeys.IMAGE,
         meta_key_postfix: str = "meta_dict",
         collate_fn: Optional[Callable] = no_collation,
-        postfix: str = "_inverted",
+        postfix: str = "inverted",
         nearest_interp: Union[bool, Sequence[bool]] = True,
         num_workers: Optional[int] = 0,
     ) -> None:
@@ -61,7 +62,7 @@ def __init__(
                 metadata `image_meta_dict` dictionary's `affine` field.
             collate_fn: how to collate data after inverse transformations.
                 default won't do any collation, so the output will be a list of size batch size.
-            postfix: will save the inverted result into `ignite.engine.output` with key `{output_key}{postfix}`.
+            postfix: will save the inverted result into `ignite.engine.output` with key `{output_key}_{postfix}`.
             nearest_interp: whether to use `nearest` interpolation mode when inverting the spatial transforms,
                 default to `True`. If `False`, use the same interpolation mode as the original transform.
                 it also can be a list of bool, each matches to the `output_keys` data.
@@ -104,7 +105,11 @@ def __call__(self, engine: Engine) -> None:
 
             transform_info = engine.state.batch[transform_key]
             if nearest_interp:
-                convert_inverse_interp_mode(trans_info=transform_info, mode="nearest", align_corners=None)
+                transform_info = convert_inverse_interp_mode(
+                    trans_info=deepcopy(transform_info),
+                    mode="nearest",
+                    align_corners=None,
+                )
 
             segs_dict = {
                 batch_key: engine.state.output[output_key].detach().cpu(),
@@ -115,5 +120,5 @@ def __call__(self, engine: Engine) -> None:
                 segs_dict[meta_dict_key] = engine.state.batch[meta_dict_key]
 
             with allow_missing_keys_mode(self.transform):  # type: ignore
-                inverted_key = f"{output_key}{self.postfix}"
+                inverted_key = f"{output_key}_{self.postfix}"
                 engine.state.output[inverted_key] = [self._totensor(i[batch_key]) for i in self.inverter(segs_dict)]
diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py
index c08b786e98..3e73beb305 100644
--- a/monai/transforms/utils.py
+++ b/monai/transforms/utils.py
@@ -800,3 +800,4 @@ def convert_inverse_interp_mode(trans_info: List, mode: str = "nearest", align_c
                     item[InverseKeys.EXTRA_INFO]["align_corners"] = [align_corners_ for _ in range(len(mode))]
                 else:
                     item[InverseKeys.EXTRA_INFO]["align_corners"] = align_corners_
+    return trans_info