ConeProjectorFunction#

class diffct.ConeProjectorFunction(*args, **kwargs)[source]#

Bases: Function

Summary#

PyTorch autograd function for differentiable 3D cone beam forward projection.

Notes

Provides a differentiable interface to the CUDA-accelerated Siddon ray-tracing method with a cell-constant voxel basis for 3D cone beam geometry. Rays emanate from a point X-ray source to a 2D detector array capturing volumetric projection data. The forward pass computes 3D projections, and the backward pass computes gradients via adjoint 3D backprojection. Requires significant GPU memory.

Examples

>>> import torch
>>> from diffct import ConeProjectorFunction, circular_trajectory_3d
>>>
>>> volume = torch.randn(128, 128, 128, device='cuda', requires_grad=True)
>>> src_pos, det_center, det_u_vec, det_v_vec = circular_trajectory_3d(360, sid=1000.0, sdd=1500.0, device='cuda')
>>> projections = ConeProjectorFunction.apply(
...     volume, src_pos, det_center, det_u_vec, det_v_vec, 256, 256, 1.0, 1.0
... )
>>> projections.sum().backward()
>>> volume.grad.shape
torch.Size([128, 128, 128])
static backward(ctx, grad_sinogram)[source]#
static forward(ctx, volume, src_pos, det_center, det_u_vec, det_v_vec, det_u, det_v, du, dv, voxel_spacing=1.0)[source]#

Compute the 3D cone beam forward projection with arbitrary trajectories using CUDA acceleration.

Parameters:

  • volume (torch.Tensor) – Floating-point CUDA volume of shape (D, H, W), converted to float32 for execution.

  • src_pos (torch.Tensor) – Source positions for each view, shape (n_views, 3), in physical units.

  • det_center (torch.Tensor) – Detector center positions for each view, shape (n_views, 3), in physical units.

  • det_u_vec (torch.Tensor) – Detector u-direction unit vectors for each view, shape (n_views, 3).

  • det_v_vec (torch.Tensor) – Detector v-direction unit vectors for each view, shape (n_views, 3).

  • det_u (int) – Number of detector elements along the u-axis (width).

  • det_v (int) – Number of detector elements along the v-axis (height).

  • du (float) – Physical spacing between detector elements along the u-axis.

  • dv (float) – Physical spacing between detector elements along the v-axis.

  • voxel_spacing (float, optional) – Physical size of one voxel (in same units as positions, default: 1.0).

Returns:

sino – 3D tensor of shape (n_views, det_u, det_v) containing the cone beam projections on the same device as volume.

Return type:

torch.Tensor

Notes

  • Geometry tensors are moved to the volume’s CUDA device and converted to float32.

  • Data derivatives support higher orders; geometry derivatives are first-order only.

  • Supports arbitrary source and detector trajectories, not limited to circular orbits.

  • Uses cell-constant Siddon ray tracing.

Examples

>>> volume = torch.randn(128, 128, 128, device='cuda', requires_grad=True)
>>> # Create circular trajectory
>>> src_pos, det_center, det_u_vec, det_v_vec = circular_trajectory_3d(360, 1000.0, 1500.0, device='cuda')
>>> sino = ConeProjectorFunction.apply(
...     volume, src_pos, det_center, det_u_vec, det_v_vec, 256, 256, 1.0, 1.0
... )
Parameters:

  • args (Any)

  • kwargs (Any)

Return type:

Any