Awesome

carterbox/torch-radon is a fork of matteo-ronchetti/torch-radon with some modules removed (shearlets, reconstruction) and the build system replaced with the extension system from PyTorch. This fork is maintained separately because the upstream project is unmaintained. If the upstream project becomes active again, this fork will attempt to merge its improvements upstream.

TorchRadon: Fast Differentiable Routines for Computed Tomography

TorchRadon is a PyTorch extension written in CUDA that implements differentiable routines for solving computed tomography (CT) reconstruction problems.

The library is designed to help researchers working on CT problems to combine deep learning and model-based approaches.

Main features:

• Forward projections, back projections and shearlet transforms are differentiable and integrated with PyTorch .backward() .
• Up to 125x faster than Astra Toolbox.
• Batch operations: fully exploit the power of modern GPUs by processing multiple images in parallel.
• Transparent API: all operations are seamlessly integrated with PyTorch, gradients can be computed using .backward() , half precision can be used with Nvidia AMP.
• Half precision: storing data in half precision allows to get sensible speedups when doing Radon forward and backward projections with a very small accuracy loss.

Implemented operations:

• Parallel Beam projections
• Fan Beam projections
• 3D Conebeam projection

Speed

TorchRadon is much faster than competing libraries:

See the Tomography Benchmarks repository for more detailed benchmarks.

Installation

Currently only Linux is supported. Windows not supported mainly because there not yet a Windows package for PyTorch on the conda-forge channel.

Install via the Conda package manager and the conda-forge channel

Please read about how to setup and use the conda package manager before attempting the following command.

conda install --channel conda-forge carterbox-torch-radon

No PYPI packages will be provided because pip was not designed for mixed-language software distribution.

Cite

If you are using TorchRadon in your research, please cite the following paper:

@article{torch_radon,
Author = {Matteo Ronchetti},
Title = {TorchRadon: Fast Differentiable Routines for Computed Tomography},
Year = {2020},
Eprint = {arXiv:2009.14788},
journal={arXiv preprint arXiv:2009.14788},
}

Testing

Install testing dependencies with pip install .[testing] then test with:

pytest tests/