Examples#

Each page has the commands, an explanation and one result figure. Figure captions state the data and settings. The scripts are in the examples/ directory of a source checkout; see the “From source” tab in Installation.

Analytical reconstruction#

Iterative reconstruction#

Clinical data and detectors#

  • Clinical helical CT: measured patient projections, flying focal spot, curved detector, 8 GPUs.

  • Curved detector: projection, matched adjoint and gradients on a native cylindrical detector.

All scripts#

The scripts in the repository examples/ directory show how to use diffct. Run them from the repository root.

  • quickstart.py: Projector basics for parallel, fan and cone beams, with the adjoint check and gradients.

  • curved_detector.py: native cylindrical cone detector projection, matched backprojection, adjoint check and volume/surface gradients on a circular scan.

  • chunked_reconstruction.py: CPU-backed CGLS with automatic CUDA tiles, optional manual limits and measured peak allocation; see Large Volumes and Chunked Execution.

  • disk_reconstruction.py: blockwise disk CGLS, optional native curves, rank-owned spatial slabs and completed-iteration checkpoints.

  • benchmark_execution.py: schedule and slab/block comparisons with actual copy payloads, pilot metadata, timing and CUDA tensor peaks.

  • benchmark_trajectory_admission.py: per-call trajectory validation without numerical projection. Use --backend torch --frame-device cpu on CPU.

  • benchmark_surface_geometry.py: forward, adjoint and geometry-gradient timings of both curved-detector interfaces on a synthetic helical scan.

  • benchmark_gpu_overheads.py: synthetic launch and transfer overheads of trajectories, detector surfaces, solvers and tiled FDK.

  • analytical_reconstruction.py: parallel-beam FBP, fan-beam FBP and cone-beam FDK, with a choice of ramp-filter window.

  • learnable_reconstruction.py: build and train a filter and detector weights for parallel/fan FBP or cone FDK using public helpers.

  • helical_fdk.py: approximate axial helical FDK with explicit slice windows and weight gradients.

  • iterative_reconstruction.py: 3D cone-beam CGLS, SIRT and TV reconstruction on circular, helical, saddle or sinusoidal trajectories.

  • walnut_reconstruction.py: FDK, SIRT, CGLS and TV reconstruction of a measured walnut scan.

  • geometry_calibration.py: recovers per-view angle errors and a detector shift from projections, using geometry gradients.

  • benchmark_projector.py: compares one GPU with several GPUs.

  • benchmark_scaling.py: measures the 512³ README workload on 1, 4 and 8 GPUs, with raw timings and numerical checks.

  • plot_trajectory.py: plots the trajectory generators (CPU only).

See examples/README.md in the repository for all launch modes.

Data sources#

  • Patient data: cases L213 (abdomen) and C002 (chest) of McCollough et al., Low Dose CT Image and Projection Data (LDCT-and-Projection-data), Version 7, The Cancer Imaging Archive, 2020, https://doi.org/10.7937/9npb-2637; dataset paper: Moen et al., Medical Physics, 2020, https://doi.org/10.1002/mp.14594. Chest and Liver subsets: CC BY 4.0. The data were acquired under grants EB017095 and EB017185 (Cynthia McCollough, PI) from the National Institute of Biomedical Imaging and Bioengineering. “Ground truth” panels show the vendor images, converted from HU to water units; all other panels are reconstructions or simulations computed with diffct.

  • Walnut: Meaney 2022, Zenodo 6986012, CC BY 4.0; see examples/data/NOTICE.

  • Shepp-Logan phantom: computed by examples/_common.py.

The figures are made by docs/assets/gallery/compute.py and docs/assets/gallery/render.py; see docs/assets/gallery/NOTICE.