diff --git a/monai/transforms/utility/array.py b/monai/transforms/utility/array.py
index 2322f2123f..5dfbcb0e91 100644
--- a/monai/transforms/utility/array.py
+++ b/monai/transforms/utility/array.py
@@ -1562,17 +1562,22 @@ def __init__(self, filter: str | NdarrayOrTensor | nn.Module, filter_size: int |
         self.filter_size = filter_size
         self.additional_args_for_filter = kwargs
 
-    def __call__(self, img: NdarrayOrTensor, meta_dict: dict | None = None) -> NdarrayOrTensor:
+    def __call__(
+        self, img: NdarrayOrTensor, meta_dict: dict | None = None, applied_operations: list | None = None
+    ) -> NdarrayOrTensor:
         """
         Args:
             img: torch tensor data to apply filter to with shape: [channels, height, width[, depth]]
             meta_dict: An optional dictionary with metadata
+            applied_operations: An optional list of operations that have been applied to the data
 
         Returns:
             A MetaTensor with the same shape as `img` and identical metadata
         """
         if isinstance(img, MetaTensor):
             meta_dict = img.meta
+            applied_operations = img.applied_operations
+
         img_, prev_type, device = convert_data_type(img, torch.Tensor)
         ndim = img_.ndim - 1  # assumes channel first format
 
@@ -1582,8 +1587,8 @@ def __call__(self, img: NdarrayOrTensor, meta_dict: dict | None = None) -> Ndarr
             self.filter = ApplyFilter(self.filter)
 
         img_ = self._apply_filter(img_)
-        if meta_dict:
-            img_ = MetaTensor(img_, meta=meta_dict)
+        if meta_dict is not None or applied_operations is not None:
+            img_ = MetaTensor(img_, meta=meta_dict, applied_operations=applied_operations)
         else:
             img_, *_ = convert_data_type(img_, prev_type, device)
         return img_
diff --git a/tests/test_image_filter.py b/tests/test_image_filter.py
index 841a5d5cd5..985ea95e79 100644
--- a/tests/test_image_filter.py
+++ b/tests/test_image_filter.py
@@ -17,6 +17,7 @@
 import torch
 from parameterized import parameterized
 
+from monai.data.meta_tensor import MetaTensor
 from monai.networks.layers.simplelayers import GaussianFilter
 from monai.transforms import ImageFilter, ImageFilterd, RandImageFilter, RandImageFilterd
 
@@ -115,6 +116,21 @@ def test_call_3d(self, filter_name):
         out_tensor = filter(SAMPLE_IMAGE_3D)
         self.assertEqual(out_tensor.shape[1:], SAMPLE_IMAGE_3D.shape[1:])
 
+    def test_pass_applied_operations(self):
+        "Test that applied operations are passed through"
+        applied_operations = ["op1", "op2"]
+        image = MetaTensor(SAMPLE_IMAGE_2D, applied_operations=applied_operations)
+        filter = ImageFilter(SUPPORTED_FILTERS[0], 3, **ADDITIONAL_ARGUMENTS)
+        out_tensor = filter(image)
+        self.assertEqual(out_tensor.applied_operations, applied_operations)
+
+    def test_pass_empty_metadata_dict(self):
+        "Test that applied operations are passed through"
+        image = MetaTensor(SAMPLE_IMAGE_2D, meta={})
+        filter = ImageFilter(SUPPORTED_FILTERS[0], 3, **ADDITIONAL_ARGUMENTS)
+        out_tensor = filter(image)
+        self.assertTrue(isinstance(out_tensor, MetaTensor))
+
 
 class TestImageFilterDict(unittest.TestCase):
     @parameterized.expand(SUPPORTED_FILTERS)