Address concerns that the rescaled pixel array from DICOM is of type float64.

In the last version, rescale slope and intercept values were tried to cast to
the smallest can_cast Nunpy type. Using pydicom apply_rescale loses this as it
returns float64 if rescale slope and intercept are applied. So, enhancing the
code will test for equivalence before "unsafely" casting the data. This will
NOT reduce the mem usage of this operator as a new array will be created by
casting from float64, and there is a small overhead for the checks. However,
there are cases where downstream custom operators may save memory footprint.

Signed-off-by: M Q <[email protected]>

Author: MMelQin

monai/deploy/operators/dicom_series_to_volume_operator.py

@@ -158,17 +158,21 @@ def generate_voxel_data(self, series):
                     f"Cannot process pixel data with Photometric Interpretation of {photometric_interpretation}."
                 )
 
-        # NumPy's np.can_cast function, as of version 2.0, no longer supports Python scalars directly and
-        # does not apply value-based logic for 0-D arrays and NumPy scalars.
-        # The following can_cast calls are expecting the array is already of the correct type.
-        if vol_data.dtype == np.uint8:
-            logging.info("Rescaled pixel data is of type uint8.")
-        elif np.can_cast(vol_data, np.uint16, casting="safe"):
-            logging.info("Casting to uint16")
-            vol_data = vol_data.astype(dtype=np.uint16, casting="safe")
-        elif np.can_cast(vol_data, np.float32, casting="safe"):
-            logging.info("Casting to float32")
-            vol_data = vol_data.astype(dtype=np.float32, casting="safe")
+        # The following tests are expecting the array is already of the Numpy type.
+        if vol_data.dtype == np.uint8 or vol_data.dtype == np.uint16:
+            logging.info("Rescaled pixel array is alrady of type uint8 or uint16.")
+        # Check if casting to uint16 and back to float results in the same values.
+        elif np.all(vol_data > 0) and np.array_equal(vol_data, vol_data.astype(np.uint16)):
+            logging.info("Rescaled pixel array can be safely cast to uint16 with equivalence test.")
+            vol_data = vol_data.astype(dtype=np.uint16)
+        # Check if casting to int16 and back to float results in the same values.
+        elif np.array_equal(vol_data, vol_data.astype(np.int16)):
+            logging.info("Rescaled pixel array can be safely cast to int16 with equivalence test.")
+            vol_data = vol_data.astype(dtype=np.int16)
+        # Check casting to float32 with equivalence test
+        elif np.array_equal(vol_data, vol_data.astype(np.float32)):
+            logging.info("Rescaled pixel array can be cast to float32 with equivalence test.")
+            vol_data = vol_data.astype(np.float32)
         else:
             logging.info("Rescaled pixel data remains as of type float64.")