v1.3.0 - Separable-footprint projector backends

Added

• Separable-footprint (SF) projector backends for 2D fan and 3D cone
  beam geometries, following Long-Fessler-Balter (IEEE TMI 2010).
  FanProjectorFunction / FanBackprojectorFunction now accept
  backend="siddon" (default, unchanged) or backend="sf".
  ConeProjectorFunction / ConeBackprojectorFunction accept
  backend="siddon" (default), "sf_tr" (trapezoid in transaxial,
  rectangle in axial) or "sf_tt" (trapezoid in both directions with
  axial rise/fall from U_near/U_far across the voxel). Each SF
  backend is implemented as a matched scatter/gather pair of CUDA
  kernels so both forward and adjoint are available — autograd works
  end-to-end for every backend, and the standalone Backprojector
  Function can also be driven in SF mode for iterative reconstruction.
• Adjoint inner-product tests for every new SF backend in
  tests/test_adjoint_inner_product.py, plus torch.autograd.gradcheck
  coverage in tests/test_gradcheck.py. Test count grew from 57 to 66.

Changed

• Internal grid-point convention is now uniformly x_v = ix - cx
  (matching _cone_3d_fdk_backproject_kernel). The SF kernels use the
  same convention so voxel centers align with where the Siddon/FDK
  paths interpret d_vol[ix, iy, iz].
• _TPB_SF_3D = (4, 4, 4) introduced for the 3D SF kernels. SF-TT
  in particular exceeds the 128-register-per-thread budget allowed by
  the default (8, 8, 8) launch; the smaller block gives each thread
  ~1024 registers and avoids CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES.

Notes

• Raw forward accuracy vs analytical Radon: on both 2D fan
  (512² Shepp-Logan) and 3D cone (96³ and 128³ 3D Shepp-Logan) phantoms,
  measured against the analytical ellipse / ellipsoid Radon transform,
  the existing Siddon projector with bilinear / trilinear interpolation
  is slightly more accurate (~3% better RMSE, ~15% better max error)
  than SF-TR / SF-TT at the thin-ray sampling level. The classic SF
  advantage reported in Long et al. is over the piecewise-constant DD
  projector; diffct's Siddon uses smooth interpolation, which covers
  most of the gap SF was designed to close.
• Full analytical reconstruction (forward + FBP/FDK): despite the
  raw-forward result above, in a complete analytical pipeline
  (FanProjectorFunction / ConeProjectorFunction -> ramp_filter_1d
  -> fan_weighted_backproject / cone_weighted_backproject) SF
  measurably improves reconstruction quality. On the 256²
  Shepp-Logan fbp_fan.py example the raw MSE drops from
  0.00216 (Siddon) to 0.00178 (SF-TR), a ~17% improvement.
  On the 128³ fdk_cone.py example it drops from 0.00325 (Siddon)
  to 0.00271 (SF-TR), again ~17%. The mechanism is that SF's
  mass-conserving cell-integrated forward pairs more naturally with
  the voxel-driven FBP/FDK gather backprojector than Siddon's
  thin-ray sampling, which feeds extra high-frequency ringing into
  the ramp filter.
• Performance: SF-TR is ~2× and SF-TT ~2.5–4× slower than Siddon for
  cone beam, and ~3× slower than Siddon for fan beam. The extra cost
  comes from voxel-driven scatter with atomic adds (forward) versus
  ray-driven parallelism in Siddon.
• Recommendation: backend="siddon" remains the default for
  backward compatibility and raw speed. For analytical FBP/FDK
  reconstruction where reconstruction MSE matters more than forward
  runtime, "sf" / "sf_tr" is a quantifiably better choice — at
  2–3× forward cost you get ~17% lower reconstruction MSE on standard
  phantoms. For iterative reconstruction with autograd, either
  backend is valid; SF has the theoretical advantage of exact voxel-
  scale mass conservation on both forward and adjoint, but in practice
  Siddon's smoothing usually helps optimizer convergence. SF-TT's
  axial-trapezoid refinement over SF-TR improves RMSE only by ~0.001
  and is not worth the extra cost for typical cone angles; use SF-TR
  by default and reserve SF-TT for extreme cone-angle research.