An update to PYRO-NN: A Python Library for Differentiable CT Operators
This work provides a software tool for researchers in medical imaging and CT reconstruction, offering incremental improvements to an existing library.
The authors updated PYRO-NN, a Python library for differentiable CT reconstruction, by adding PyTorch compatibility, CUDA kernel support for efficient operations across various geometries, and tools for simulating artifacts and modeling acquisition trajectories, enabling flexible, end-to-end trainable pipelines.
Deep learning has brought significant advancements to X-ray Computed Tomography (CT) reconstruction, offering solutions to challenges arising from modern imaging technologies. These developments benefit from methods that combine classical reconstruction techniques with data-driven approaches. Differentiable operators play a key role in this integration by enabling end-to-end optimization and the incorporation of physical modeling within neural networks. In this work, we present an updated version of PYRO-NN, a Python-based library for differentiable CT reconstruction. The updated framework extends compatibility to PyTorch and introduces native CUDA kernel support for efficient projection and back-projection operations across parallel, fan, and cone-beam geometries. Additionally, it includes tools for simulating imaging artifacts, modeling arbitrary acquisition trajectories, and creating flexible, end-to-end trainable pipelines through a high-level Python API. Code is available at: https://github.com/csyben/PYRO-NN