Cleanup tutorials

diffdrr/siddon.py

@@ -14,7 +14,7 @@ def siddon_raycast(
     spacing: torch.Tensor,
     eps: float = 1e-8,
 ):
-    """Compute Siddon's method."""
+    """An auto-differentiable implementation of the raycasting algorithm known as Siddon's method."""
     dims = torch.tensor(volume.shape) + 1
     alphas, maxidx = _get_alphas(source, target, spacing, dims, eps)
     alphamid = (alphas[..., 0:-1] + alphas[..., 1:]) / 2

notebooks/api/01_siddon.ipynb

@@ -90,7 +90,7 @@
     "    spacing: torch.Tensor,\n",
     "    eps: float=1e-8,\n",
     "):\n",
-    "    \"\"\"Compute Siddon's method.\"\"\"\n",
+    "    \"\"\"An auto-differentiable implementation of the raycasting algorithm known as Siddon's method.\"\"\"\n",
     "    dims = torch.tensor(volume.shape) + 1\n",
     "    alphas, maxidx = _get_alphas(source, target, spacing, dims, eps)\n",
     "    alphamid = (alphas[..., 0:-1] + alphas[..., 1:]) / 2\n",

notebooks/tutorials/introduction.ipynb

@@ -117,7 +117,17 @@
    "source": [
     "### Sparse rendering\n",
     "\n",
-    "You can also render random sparse subsets of the pixels in a DRR. This is useful for speeding up registration tasks."
+    "You can also render random sparse subsets of the pixels in a DRR."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "296277be-6b59-4f77-b846-b1521d300221",
+   "metadata": {},
+   "source": [
+    "::: {.callout-tip}\n",
+    "Sparse DRR rendering can be useful in registration and reconstruction tasks when coupled with a pixel-wise loss, such as MSE.\n",
+    ":::"
    ]
   },
   {

notebooks/tutorials/timing.ipynb

@@ -75,7 +75,7 @@
     "height = 100\n",
     "\n",
     "drr = DRR(volume, spacing, sdr=300.0, height=height, delx=4.0).to(device)\n",
-    "%timeit drr(rotations, translations)\n",
+    "!%timeit drr(rotations, translations)\n",
     "del drr"
    ]
   },