Merge pull request #187 from eigenvivek/trilinear-renderer

Trilinear renderer

diffdrr/_modidx.py

@@ -104,11 +104,20 @@
                               'diffdrr.pose.so3_relative_angle': ('api/pose.html#so3_relative_angle', 'diffdrr/pose.py'),
                               'diffdrr.pose.so3_rotation_angle': ('api/pose.html#so3_rotation_angle', 'diffdrr/pose.py'),
                               'diffdrr.pose.standardize_quaternion': ('api/pose.html#standardize_quaternion', 'diffdrr/pose.py')},
-            'diffdrr.siddon': { 'diffdrr.siddon._get_alpha_minmax': ('api/siddon.html#_get_alpha_minmax', 'diffdrr/siddon.py'),
-                                'diffdrr.siddon._get_alphas': ('api/siddon.html#_get_alphas', 'diffdrr/siddon.py'),
-                                'diffdrr.siddon._get_index': ('api/siddon.html#_get_index', 'diffdrr/siddon.py'),
-                                'diffdrr.siddon._get_voxel': ('api/siddon.html#_get_voxel', 'diffdrr/siddon.py'),
-                                'diffdrr.siddon.siddon_raycast': ('api/siddon.html#siddon_raycast', 'diffdrr/siddon.py')},
+            'diffdrr.renderers': { 'diffdrr.renderers.Siddon': ('api/renderers.html#siddon', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Siddon.__init__': ('api/renderers.html#siddon.__init__', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Siddon.dims': ('api/renderers.html#siddon.dims', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Siddon.forward': ('api/renderers.html#siddon.forward', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Siddon.maxidx': ('api/renderers.html#siddon.maxidx', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Trilinear': ('api/renderers.html#trilinear', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Trilinear.__init__': ( 'api/renderers.html#trilinear.__init__',
+                                                                             'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Trilinear.dims': ('api/renderers.html#trilinear.dims', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers.Trilinear.forward': ('api/renderers.html#trilinear.forward', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers._get_alpha_minmax': ('api/renderers.html#_get_alpha_minmax', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers._get_alphas': ('api/renderers.html#_get_alphas', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers._get_index': ('api/renderers.html#_get_index', 'diffdrr/renderers.py'),
+                                   'diffdrr.renderers._get_voxel': ('api/renderers.html#_get_voxel', 'diffdrr/renderers.py')},
             'diffdrr.utils': { 'diffdrr.utils.get_focal_length': ('api/utils.html#get_focal_length', 'diffdrr/utils.py'),
                                'diffdrr.utils.get_principal_point': ('api/utils.html#get_principal_point', 'diffdrr/utils.py'),
                                'diffdrr.utils.parse_intrinsic_matrix': ('api/utils.html#parse_intrinsic_matrix', 'diffdrr/utils.py')},

diffdrr/drr.py

@@ -9,7 +9,7 @@
 from fastcore.basics import patch
 
 from .detector import Detector
-from .siddon import siddon_raycast
+from .renderers import Siddon, Trilinear
 
 # %% auto 0
 __all__ = ['DRR', 'Registration']
@@ -34,6 +34,8 @@ def __init__(
         reverse_x_axis: bool = False,  # If pose includes reflection (in E(3) not SE(3)), reverse x-axis
         patch_size: int | None = None,  # Render patches of the DRR in series
         bone_attenuation_multiplier: float = 1.0,  # Contrast ratio of bone to soft tissue
+        renderer: str = "siddon",  # Rendering backend, either "siddon" or "trilinear"
+        **renderer_kwargs,  # Kwargs for the renderer
     ):
         super().__init__()
 
@@ -69,6 +71,14 @@ def __init__(
         self.bone = torch.where(350 < self.volume)
         self.bone_attenuation_multiplier = bone_attenuation_multiplier
 
+        # Initialize the renderer
+        if renderer == "siddon":
+            self.renderer = Siddon(**renderer_kwargs)
+        elif renderer == "trilinear":
+            self.renderer = Trilinear(**renderer_kwargs)
+        else:
+            raise ValueError(f"renderer must be 'siddon', not {renderer}")
+
     def reshape_transform(self, img, batch_size):
         if self.reshape:
             if self.detector.n_subsample is None:
@@ -101,6 +111,7 @@ def forward(
     parameterization: str = None,  # Specifies the representation of the rotation
     convention: str = None,  # If parameterization is Euler angles, specify convention
     bone_attenuation_multiplier: float = None,  # Contrast ratio of bone to soft tissue
+    **kwargs,  # Passed to the renderer
 ):
     """Generate DRR with rotational and translational parameters."""
     if not hasattr(self, "density"):
@@ -119,11 +130,11 @@ def forward(
         img = []
         for idx in range(self.n_patches):
             t = target[:, idx * n_points : (idx + 1) * n_points]
-            partial = siddon_raycast(source, t, self.density, self.spacing)
+            partial = self.renderer(self.density, self.spacing, source, t, **kwargs)
             img.append(partial)
         img = torch.cat(img, dim=1)
     else:
-        img = siddon_raycast(source, target, self.density, self.spacing)
+        img = self.renderer(self.density, self.spacing, source, target, **kwargs)
     return self.reshape_transform(img, batch_size=len(pose))
 
 # %% ../notebooks/api/00_drr.ipynb 11

diffdrr/siddon.py → diffdrr/renderers.py

@@ -1,37 +1,44 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/01_siddon.ipynb.
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../notebooks/api/01_renderers.ipynb.
 
 # %% auto 0
-__all__ = ['siddon_raycast']
+__all__ = ['Siddon', 'Trilinear']
 
-# %% ../notebooks/api/01_siddon.ipynb 3
+# %% ../notebooks/api/01_renderers.ipynb 3
 import torch
 
-# %% ../notebooks/api/01_siddon.ipynb 6
-def siddon_raycast(
-    source: torch.Tensor,
-    target: torch.Tensor,
-    volume: torch.Tensor,
-    spacing: torch.Tensor,
-    eps: float = 1e-8,
-):
-    """An auto-differentiable implementation of the raycasting algorithm known as Siddon's method."""
-    maxidx = volume.numel() - 1
-    dims = torch.tensor(volume.shape).to(source) + 1
-    alphas = _get_alphas(source, target, spacing, dims, eps)
-    alphamid = (alphas[..., 0:-1] + alphas[..., 1:]) / 2
-    voxels = _get_voxel(alphamid, source, target, volume, spacing, dims, maxidx, eps)
-
-    # Step length for alphas out of range will be nan
-    # These nans cancel out voxels convereted to 0 index
-    step_length = torch.diff(alphas, dim=-1)
-    weighted_voxels = voxels * step_length
-
-    drr = torch.nansum(weighted_voxels, dim=-1)
-    raylength = (target - source + eps).norm(dim=-1)
-    drr *= raylength
-    return drr
-
-# %% ../notebooks/api/01_siddon.ipynb 8
+# %% ../notebooks/api/01_renderers.ipynb 6
+class Siddon(torch.nn.Module):
+    def __init__(self, eps=1e-8):
+        super().__init__()
+        self.eps = eps
+
+    def dims(self, volume):
+        return torch.tensor(volume.shape).to(volume) + 1
+
+    def maxidx(self, volume):
+        return volume.numel() - 1
+
+    def forward(self, volume, spacing, source, target):
+        dims = self.dims(volume)
+        maxidx = self.maxidx(volume)
+
+        alphas = _get_alphas(source, target, spacing, dims, self.eps)
+        alphamid = (alphas[..., 0:-1] + alphas[..., 1:]) / 2
+        voxels = _get_voxel(
+            alphamid, source, target, volume, spacing, dims, maxidx, self.eps
+        )
+
+        # Step length for alphas out of range will be nan
+        # These nans cancel out voxels convereted to 0 index
+        step_length = torch.diff(alphas, dim=-1)
+        weighted_voxels = voxels * step_length
+
+        drr = torch.nansum(weighted_voxels, dim=-1)
+        raylength = (target - source + self.eps).norm(dim=-1)
+        drr *= raylength
+        return drr
+
+# %% ../notebooks/api/01_renderers.ipynb 8
 def _get_alphas(source, target, spacing, dims, eps):
     # Get the CT sizing and spacing parameters
     dx, dy, dz = spacing
@@ -100,3 +107,50 @@ def _get_index(alpha, source, target, spacing, dims, maxidx, eps):
     idxs[idxs < 0] = 0
     idxs[idxs > maxidx] = maxidx
     return idxs
+
+# %% ../notebooks/api/01_renderers.ipynb 10
+from torch.nn.functional import grid_sample
+
+
+class Trilinear(torch.nn.Module):
+    def __init__(
+        self,
+        near=0.0,
+        far=1.0,
+        eps=1e-8,
+        mode="bilinear",
+    ):
+        super().__init__()
+        self.near = near
+        self.far = far
+        self.eps = eps
+        self.mode = mode
+
+    def dims(self, volume):
+        return torch.tensor(volume.shape).to(volume) + 1
+
+    def forward(
+        self, volume, spacing, source, target, n_points=100, align_corners=True
+    ):
+        # Reorder array to match torch conventions
+        volume = volume.permute(2, 1, 0)
+        spacing = spacing[[2, 1, 0]]
+
+        # Get the raylength and reshape sources
+        raylength = (source - target + self.eps).norm(dim=-1)
+        source = source[:, None, :, None, :]
+        target = target[:, None, :, None, :]
+
+        # Sample points along the rays and rescale to [-1, 1]
+        alphas = torch.linspace(self.near, self.far, n_points).to(volume)
+        alphas = alphas[None, None, None, :, None]
+        rays = source + alphas * (target - source)
+        rays = 2 * rays / (spacing * self.dims(volume)) - 1
+
+        # Render the DRR
+        batch_size = len(rays)
+        vol = volume[None, None, :, :, :].expand(batch_size, -1, -1, -1, -1)
+        drr = grid_sample(vol, rays, mode=self.mode, align_corners=align_corners)
+        drr = drr[:, 0, 0].sum(dim=-1)
+        drr *= raylength
+        return drr

notebooks/api/00_drr.ipynb

@@ -49,7 +49,7 @@
     "from fastcore.basics import patch\n",
     "\n",
     "from diffdrr.detector import Detector\n",
-    "from diffdrr.siddon import siddon_raycast"
+    "from diffdrr.renderers import Siddon, Trilinear"
    ]
   },
   {
@@ -129,6 +129,8 @@
     "        reverse_x_axis: bool = False,  # If pose includes reflection (in E(3) not SE(3)), reverse x-axis\n",
     "        patch_size: int | None = None,  # Render patches of the DRR in series\n",
     "        bone_attenuation_multiplier: float = 1.0,  # Contrast ratio of bone to soft tissue\n",
+    "        renderer: str = \"siddon\",  # Rendering backend, either \"siddon\" or \"trilinear\"\n",
+    "        **renderer_kwargs,  # Kwargs for the renderer\n",
     "    ):\n",
     "        super().__init__()\n",
     "\n",
@@ -164,6 +166,14 @@
     "        self.bone = torch.where(350 < self.volume)\n",
     "        self.bone_attenuation_multiplier = bone_attenuation_multiplier\n",
     "\n",
+    "        # Initialize the renderer\n",
+    "        if renderer == \"siddon\":\n",
+    "            self.renderer = Siddon(**renderer_kwargs)\n",
+    "        elif renderer == \"trilinear\":\n",
+    "            self.renderer = Trilinear(**renderer_kwargs)\n",
+    "        else:\n",
+    "            raise ValueError(f\"renderer must be 'siddon', not {renderer}\")\n",
+    "\n",
     "    def reshape_transform(self, img, batch_size):\n",
     "        if self.reshape:\n",
     "            if self.detector.n_subsample is None:\n",
@@ -220,6 +230,7 @@
     "    parameterization: str = None,  # Specifies the representation of the rotation\n",
     "    convention: str = None,  # If parameterization is Euler angles, specify convention\n",
     "    bone_attenuation_multiplier: float = None,  # Contrast ratio of bone to soft tissue\n",
+    "    **kwargs,  # Passed to the renderer\n",
     "):\n",
     "    \"\"\"Generate DRR with rotational and translational parameters.\"\"\"\n",
     "    if not hasattr(self, \"density\"):\n",
@@ -238,11 +249,11 @@
     "        img = []\n",
     "        for idx in range(self.n_patches):\n",
     "            t = target[:, idx * n_points : (idx + 1) * n_points]\n",
-    "            partial = siddon_raycast(source, t, self.density, self.spacing)\n",
+    "            partial = self.renderer(self.density, self.spacing, source, t, **kwargs)\n",
     "            img.append(partial)\n",
     "        img = torch.cat(img, dim=1)\n",
     "    else:\n",
-    "        img = siddon_raycast(source, target, self.density, self.spacing)\n",
+    "        img = self.renderer(self.density, self.spacing, source, target, **kwargs)\n",
     "    return self.reshape_transform(img, batch_size=len(pose))"
    ]
   },