diff --git a/monai/inferers/utils.py b/monai/inferers/utils.py
index 45742eff79..b171d20ebb 100644
--- a/monai/inferers/utils.py
+++ b/monai/inferers/utils.py
@@ -41,7 +41,7 @@
 
 
 def sliding_window_inference(
-    inputs: torch.Tensor,
+    inputs: torch.Tensor | MetaTensor,
     roi_size: Sequence[int] | int,
     sw_batch_size: int,
     predictor: Callable[..., torch.Tensor | Sequence[torch.Tensor] | dict[Any, torch.Tensor]],
@@ -307,9 +307,11 @@ def sliding_window_inference(
             output_image_list[ss] = output_i[(slice(None), slice(None), *final_slicing)]
 
     final_output = _pack_struct(output_image_list, dict_keys)
-    final_output = convert_to_dst_type(final_output, inputs, device=device)[0]
     if temp_meta is not None:
-        final_output = MetaTensor(final_output).copy_meta_from(temp_meta)
+        final_output = convert_to_dst_type(final_output, temp_meta, device=device)[0]  # type: ignore
+    else:
+        final_output = convert_to_dst_type(final_output, inputs, device=device)[0]
+
     return final_output  # type: ignore
 
 
diff --git a/tests/test_sliding_window_hovernet_inference.py b/tests/test_sliding_window_hovernet_inference.py
index 8f7f8346cc..b17e8525ec 100644
--- a/tests/test_sliding_window_hovernet_inference.py
+++ b/tests/test_sliding_window_hovernet_inference.py
@@ -18,6 +18,7 @@
 from parameterized import parameterized
 
 from monai.apps.pathology.inferers import SlidingWindowHoVerNetInferer
+from monai.data import MetaTensor
 from monai.inferers import sliding_window_inference
 from monai.utils import optional_import
 from tests.test_sliding_window_inference import TEST_CASES
@@ -31,6 +32,8 @@
     ["hover", (1, 3, 16, 8), (4, 4), 7, 0.5, "constant", torch.device("cpu:0"), (1,) * 4],
 ]
 
+TEST_CASES_MULTIOUTPUT = [[torch.ones((1, 6, 20, 20))], [MetaTensor(torch.ones((1, 6, 20, 20)))]]
+
 
 class TestSlidingWindowHoVerNetInference(unittest.TestCase):
     @parameterized.expand(TEST_CASES_PADDING)
@@ -245,9 +248,10 @@ def compute(data, test1, test2):
         )(inputs, compute, t1, test2=t2)
         np.testing.assert_allclose(result.cpu().numpy(), expected, rtol=1e-4)
 
-    def test_multioutput(self):
+    @parameterized.expand(TEST_CASES_MULTIOUTPUT)
+    def test_multioutput(self, inputs):
         device = "cuda" if torch.cuda.is_available() else "cpu:0"
-        inputs = torch.ones((1, 6, 20, 20)).to(device=device)
+        inputs = inputs.to(device=device)
         roi_shape = (8, 8)
         sw_batch_size = 10