diff --git a/diffdrr/renderers.py b/diffdrr/renderers.py
index 4d1b691fc..6eb87bff2 100644
--- a/diffdrr/renderers.py
+++ b/diffdrr/renderers.py
@@ -182,13 +182,16 @@ def forward(
         n_points=500,
         align_corners=False,
         mask=None,
+        alphamin=None,
+        alphamax=None,
     ):
         dims = self.dims(volume)
 
         # Sample points along the rays and rescale to [-1, 1]
-        alphamin, alphamax = _get_alpha_minmax(source, target, dims, self.eps)
-        alphamin = alphamin.min()
-        alphamax = alphamax.max()
+        if alphamin is None or alphamax is None:
+            alphamin, alphamax = _get_alpha_minmax(source, target, dims, self.eps)
+            alphamin = alphamin.min()
+            alphamax = alphamax.max()
         alphas = torch.linspace(0, 1, n_points)[None, None].to(volume)
         alphas = alphas * (alphamax - alphamin) + alphamin
 
diff --git a/notebooks/api/01_renderers.ipynb b/notebooks/api/01_renderers.ipynb
index 1947303f9..dab23f566 100644
--- a/notebooks/api/01_renderers.ipynb
+++ b/notebooks/api/01_renderers.ipynb
@@ -175,7 +175,9 @@
     "            # https://stackoverflow.com/questions/78323859/broadcast-pytorch-array-across-channels-based-on-another-array/78324614#78324614\n",
     "            B, D, _ = img.shape\n",
     "            C = int(mask.max().item() + 1)\n",
-    "            channels = _get_voxel(mask, xyzs, self.mode, align_corners=align_corners).long()\n",
+    "            channels = _get_voxel(\n",
+    "                mask, xyzs, self.mode, align_corners=align_corners\n",
+    "            ).long()\n",
     "            img = (\n",
     "                torch.zeros(B, C, D)\n",
     "                .to(img)\n",
@@ -311,13 +313,16 @@
     "        n_points=500,\n",
     "        align_corners=False,\n",
     "        mask=None,\n",
+    "        alphamin=None,\n",
+    "        alphamax=None,\n",
     "    ):\n",
     "        dims = self.dims(volume)\n",
     "\n",
     "        # Sample points along the rays and rescale to [-1, 1]\n",
-    "        alphamin, alphamax = _get_alpha_minmax(source, target, dims, self.eps)\n",
-    "        alphamin = alphamin.min()\n",
-    "        alphamax = alphamax.max()\n",
+    "        if alphamin is None or alphamax is None:\n",
+    "            alphamin, alphamax = _get_alpha_minmax(source, target, dims, self.eps)\n",
+    "            alphamin = alphamin.min()\n",
+    "            alphamax = alphamax.max()\n",
     "        alphas = torch.linspace(0, 1, n_points)[None, None].to(volume)\n",
     "        alphas = alphas * (alphamax - alphamin) + alphamin\n",
     "\n",
@@ -334,7 +339,9 @@
     "        else:\n",
     "            B, D, _ = img.shape\n",
     "            C = int(mask.max().item() + 1)\n",
-    "            channels = _get_voxel(mask, xyzs, self.mode, align_corners=align_corners).long()\n",
+    "            channels = _get_voxel(\n",
+    "                mask, xyzs, self.mode, align_corners=align_corners\n",
+    "            ).long()\n",
     "            img = (\n",
     "                torch.zeros(B, C, D)\n",
     "                .to(img)\n",