An update to PYRO-NN: A Python Library for Differentiable CT Operators

• URL: http://arxiv.org/abs/2511.08427v1
• Date: Wed, 12 Nov 2025 01:58:22 GMT
• Title: An update to PYRO-NN: A Python Library for Differentiable CT Operators
• Authors: Linda-Sophie Schneider, Yipeng Sun, Chengze Ye, Markus Michen, Andreas Maier,
• Abstract summary: Deep learning has brought significant advancements to X-ray Computed Tomography (CT) reconstruction.<n>We present an updated version of PYRO-NN, a Python-based library for differentiable CT reconstruction.
• Score: 3.5218270958038365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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