diff --git a/monai/transforms/croppad/array.py b/monai/transforms/croppad/array.py
index b23fbac7d9..9fd1283afd 100644
--- a/monai/transforms/croppad/array.py
+++ b/monai/transforms/croppad/array.py
@@ -170,7 +170,7 @@ def inverse(self, data: MetaTensor) -> MetaTensor:
         padded = transform[TraceKeys.EXTRA_INFO]["padded"]
         if padded[0][0] > 0 or padded[0][1] > 0:  # slicing the channel dimension
             s = padded[0][0]
-            e = min(max(padded[0][1], s + 1), len(data))
+            e = min(padded[0][1], len(data) - s)
             data = data[s : len(data) - e]  # type: ignore
         roi_start = [i[0] for i in padded[1:]]
         roi_end = [i - j[1] for i, j in zip(data.shape[1:], padded[1:])]
diff --git a/tests/transforms/croppad/test_pad_inverse_channel.py b/tests/transforms/croppad/test_pad_inverse_channel.py
new file mode 100644
index 0000000000..e4a2730630
--- /dev/null
+++ b/tests/transforms/croppad/test_pad_inverse_channel.py
@@ -0,0 +1,44 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+import unittest
+
+import torch
+from parameterized.parameterized import parameterized
+
+from monai.data import MetaTensor, set_track_meta
+from monai.transforms.croppad.array import Pad
+
+# "symmetric" routes through the NumPy backend, which pads the channel dim.
+CHANNEL_PAD_CASES = [
+    [[(2, 0), (0, 0), (0, 0)]],
+    [[(0, 2), (0, 0), (0, 0)]],
+    [[(2, 2), (0, 0), (0, 0)]],
+    [[(1, 3), (1, 1), (0, 2)]],
+]
+
+
+class TestPadInverseChannel(unittest.TestCase):
+    @parameterized.expand(CHANNEL_PAD_CASES)
+    def test_inverse_roundtrip_channel_pad(self, to_pad):
+        set_track_meta(True)
+        img = MetaTensor(torch.arange(3 * 4 * 4).reshape(3, 4, 4).float())
+        pad = Pad(to_pad=to_pad, mode="symmetric")
+        padded = pad(img.clone())
+        self.assertEqual(padded.shape[0], img.shape[0] + to_pad[0][0] + to_pad[0][1])
+        inv = pad.inverse(padded)
+        self.assertEqual(inv.shape, img.shape)
+        self.assertTrue(torch.equal(inv.as_tensor(), img.as_tensor()))
+
+
+if __name__ == "__main__":
+    unittest.main()