diffct 1.2.3

A CUDA-based library for computed tomography (CT) projection and reconstruction with differentiable operators

diffct: Differentiable Computed Tomography Operators

A high-performance, CUDA-accelerated library for circular orbits CT reconstruction with end-to-end differentiable operators, enabling advanced optimization and deep learning integration.

⭐ Please star this project if you find it is useful!

✨ Features

• Fast: CUDA-accelerated projection and backprojection operations
• Differentiable: End-to-end gradient propagation for deep learning workflows

📐 Supported Geometries

• Parallel Beam: 2D parallel-beam geometry
• Fan Beam: 2D fan-beam geometry
• Cone Beam: 3D cone-beam geometry

🧩 Code Structure

diffct/
├── diffct/
│   ├── __init__.py            # Package initialization
│   ├── differentiable.py      # Differentiable CT operators
├── examples/                  # Example usages
│   ├── fbp_parallel.py
│   ├── fbp_fan.py
│   ├── fdk_cone.py
│   ├── iterative_reco_cone.py
│   ├── iterative_reco_fan.py
│   ├── iterative_reco_parallel.py
├── pyproject.toml             # Project metadata
├── README.md                  # README
├── LICENSE                    # License
├── requirements.txt           # Dependencies

🚀 Quick Start

Prerequisites

• CUDA-capable GPU
• Python 3.10+
• PyTorch, NumPy, Numba, CUDA

Installation

pip install diffct

📝 Citation

If you use this library in your research, please cite:

@software{diffct2025,
  author       = {Yipeng Sun},
  title        = {diffct: Differentiable Computed Tomography 
                 Reconstruction with CUDA},
  year         = 2025,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.14999333},
  url          = {https://doi.org/10.5281/zenodo.14999333}
}

📄 License

This project is licensed under the Apache 2.0 - see the LICENSE file for details.

🙏 Acknowledgements

This project was highly inspired by:

• PYRO-NN
• geometry_gradients_CT

Issues and contributions are welcome!