diff --git a/GANDLF/cli/preprocess_and_save.py b/GANDLF/cli/preprocess_and_save.py
index 82bc4f574..1da202ae7 100644
--- a/GANDLF/cli/preprocess_and_save.py
+++ b/GANDLF/cli/preprocess_and_save.py
@@ -105,15 +105,13 @@ def preprocess_and_save(
             # the "squeeze" is needed because the dataloader automatically
             # constructs 5D tensor considering the batch_size as first
             # dimension, but the constructor needs 4D tensor.
-            subject_process[str(channel)] = torchio.Image(
+            subject_process[str(channel)] = torchio.ScalarImage(
                 tensor=subject[str(channel)]["data"].squeeze(0),
-                type=torchio.INTENSITY,
                 path=subject[str(channel)]["path"],
             )
         if parameters["headers"]["labelHeader"] is not None:
-            subject_process["label"] = torchio.Image(
+            subject_process["label"] = torchio.LabelMap(
                 tensor=subject["label"]["data"].squeeze(0),
-                type=torchio.LABEL,
                 path=subject["label"]["path"],
             )
         subject_dict_to_write = torchio.Subject(subject_process)
diff --git a/GANDLF/compute/forward_pass.py b/GANDLF/compute/forward_pass.py
index 0adcc5fb3..82cebdaa4 100644
--- a/GANDLF/compute/forward_pass.py
+++ b/GANDLF/compute/forward_pass.py
@@ -129,12 +129,12 @@ def validate_network(
         subject_dict = {}
         label_ground_truth = None
         label_present = False
-        # this is when we want the dataloader to pick up properties of GaNDLF's DataLoader, such as pre-processing and augmentations, if appropriate
+        # this is when we want the dataloader to pick up properties of GaNDLF's
+        # DataLoader, such as pre-processing and augmentations, if appropriate
         if "label" in subject:
             if subject["label"] != ["NA"]:
-                subject_dict["label"] = torchio.Image(
+                subject_dict["label"] = torchio.LabelMap(
                     path=subject["label"]["path"],
-                    type=torchio.LABEL,
                     tensor=subject["label"]["data"].squeeze(0),
                     affine=subject["label"]["affine"].squeeze(0),
                 )
@@ -149,9 +149,8 @@ def validate_network(
                 )
 
         for key in params["channel_keys"]:
-            subject_dict[key] = torchio.Image(
+            subject_dict[key] = torchio.ScalarImage(
                 path=subject[key]["path"],
-                type=subject[key]["type"],
                 tensor=subject[key]["data"].squeeze(0),
                 affine=subject[key]["affine"].squeeze(0),
             )
@@ -320,8 +319,7 @@ def validate_network(
                 output_prediction = aggregator.get_output_tensor()
                 output_prediction = output_prediction.unsqueeze(0)
                 if params["save_output"]:
-                    img_for_metadata = torchio.Image(
-                        type=subject["1"]["type"],
+                    img_for_metadata = torchio.ScalarImage(
                         tensor=subject["1"]["data"].squeeze(0),
                         affine=subject["1"]["affine"].squeeze(0),
                     ).as_sitk()
diff --git a/GANDLF/data/augmentation/noise_enhanced.py b/GANDLF/data/augmentation/noise_enhanced.py
index 2a9a21e17..41fb6ed74 100644
--- a/GANDLF/data/augmentation/noise_enhanced.py
+++ b/GANDLF/data/augmentation/noise_enhanced.py
@@ -59,8 +59,8 @@ def get_params(
         mean_range: Tuple[float, float],
         std_range: Tuple[float, float],
     ) -> Tuple[float, float]:
-        mean = self.sample_uniform(*mean_range).item()
-        std = self.sample_uniform(*std_range).item()
+        mean = self.sample_uniform(*mean_range)
+        std = self.sample_uniform(*std_range)
         seed = self._get_random_seed()
         return mean, std, seed
 
diff --git a/setup.py b/setup.py
index 4e02b6e79..cc49ede40 100644
--- a/setup.py
+++ b/setup.py
@@ -89,7 +89,7 @@ def run(self):
     "SimpleITK!=2.2.1",  # https://github.com/mlcommons/GaNDLF/issues/536
     "torchvision",
     "tqdm",
-    "torchio==0.18.75",
+    "torchio==0.19.3",
     "pandas>=2.0.0",
     "scikit-learn>=0.23.2",
     "scikit-image>=0.19.1",