diff --git a/reproduction_effort/functions/align_refref.py b/reproduction_effort/functions/align_refref.py
index 401fc60..094b09c 100644
--- a/reproduction_effort/functions/align_refref.py
+++ b/reproduction_effort/functions/align_refref.py
@@ -12,7 +12,7 @@ def align_refref(
     ref_image_donor: torch.Tensor,
     image_alignment: ImageAlignment,
     batch_size: int,
-    fill_value: int = 0,
+    fill_value: float = 0,
 ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
 
     angle_refref = calculate_rotation(
diff --git a/reproduction_effort/functions/perform_donor_volume_rotation.py b/reproduction_effort/functions/perform_donor_volume_rotation.py
new file mode 100644
index 0000000..519b111
--- /dev/null
+++ b/reproduction_effort/functions/perform_donor_volume_rotation.py
@@ -0,0 +1,84 @@
+import torch
+import torchvision as tv  # type: ignore
+from functions.calculate_rotation import calculate_rotation
+from functions.ImageAlignment import ImageAlignment
+
+
+@torch.no_grad()
+def perform_donor_volume_rotation(
+    acceptor: torch.Tensor,
+    donor: torch.Tensor,
+    oxygenation: torch.Tensor,
+    volume: torch.Tensor,
+    ref_image_donor: torch.Tensor,
+    ref_image_volume: torch.Tensor,
+    image_alignment: ImageAlignment,
+    batch_size: int,
+    fill_value: float = 0,
+) -> tuple[
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+]:
+
+    angle_donor = calculate_rotation(
+        input=donor,
+        reference_image=ref_image_donor,
+        image_alignment=image_alignment,
+        batch_size=batch_size,
+    )
+
+    angle_volume = calculate_rotation(
+        input=volume,
+        reference_image=ref_image_volume,
+        image_alignment=image_alignment,
+        batch_size=batch_size,
+    )
+
+    angle_donor_volume = (angle_donor + angle_volume) / 2.0
+
+    for frame_id in range(0, angle_donor_volume.shape[0]):
+
+        acceptor[frame_id, ...] = tv.transforms.functional.affine(
+            img=acceptor[frame_id, ...].unsqueeze(0),
+            angle=-float(angle_donor_volume[frame_id]),
+            translate=[0, 0],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+        donor[frame_id, ...] = tv.transforms.functional.affine(
+            img=donor[frame_id, ...].unsqueeze(0),
+            angle=-float(angle_donor_volume[frame_id]),
+            translate=[0, 0],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+        oxygenation[frame_id, ...] = tv.transforms.functional.affine(
+            img=oxygenation[frame_id, ...].unsqueeze(0),
+            angle=-float(angle_donor_volume[frame_id]),
+            translate=[0, 0],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+        volume[frame_id, ...] = tv.transforms.functional.affine(
+            img=volume[frame_id, ...].unsqueeze(0),
+            angle=-float(angle_donor_volume[frame_id]),
+            translate=[0, 0],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+    return (acceptor, donor, oxygenation, volume, angle_donor_volume)
diff --git a/reproduction_effort/functions/perform_donor_volume_translation.py b/reproduction_effort/functions/perform_donor_volume_translation.py
new file mode 100644
index 0000000..b43ff95
--- /dev/null
+++ b/reproduction_effort/functions/perform_donor_volume_translation.py
@@ -0,0 +1,84 @@
+import torch
+import torchvision as tv  # type: ignore
+from functions.calculate_translation import calculate_translation
+from functions.ImageAlignment import ImageAlignment
+
+
+@torch.no_grad()
+def perform_donor_volume_translation(
+    acceptor: torch.Tensor,
+    donor: torch.Tensor,
+    oxygenation: torch.Tensor,
+    volume: torch.Tensor,
+    ref_image_donor: torch.Tensor,
+    ref_image_volume: torch.Tensor,
+    image_alignment: ImageAlignment,
+    batch_size: int,
+    fill_value: float = 0,
+) -> tuple[
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+    torch.Tensor,
+]:
+
+    tvec_donor = calculate_translation(
+        input=donor,
+        reference_image=ref_image_donor,
+        image_alignment=image_alignment,
+        batch_size=batch_size,
+    )
+
+    tvec_volume = calculate_translation(
+        input=volume,
+        reference_image=ref_image_volume,
+        image_alignment=image_alignment,
+        batch_size=batch_size,
+    )
+
+    tvec_donor_volume = (tvec_donor + tvec_volume) / 2.0
+
+    for frame_id in range(0, tvec_donor_volume.shape[0]):
+
+        acceptor[frame_id, ...] = tv.transforms.functional.affine(
+            img=acceptor[frame_id, ...].unsqueeze(0),
+            angle=0,
+            translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+        donor[frame_id, ...] = tv.transforms.functional.affine(
+            img=donor[frame_id, ...].unsqueeze(0),
+            angle=0,
+            translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+        oxygenation[frame_id, ...] = tv.transforms.functional.affine(
+            img=oxygenation[frame_id, ...].unsqueeze(0),
+            angle=0,
+            translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+        volume[frame_id, ...] = tv.transforms.functional.affine(
+            img=volume[frame_id, ...].unsqueeze(0),
+            angle=0,
+            translate=[tvec_donor_volume[frame_id, 1], tvec_donor_volume[frame_id, 0]],
+            scale=1.0,
+            shear=0,
+            interpolation=tv.transforms.InterpolationMode.BILINEAR,
+            fill=fill_value,
+        ).squeeze(0)
+
+    return (acceptor, donor, oxygenation, volume, tvec_donor_volume)