{"ID":2843511,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08427","arxiv_id":"2511.08427","title":"An update to PYRO-NN: A Python Library for Differentiable CT Operators","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","short_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 p...","url_abs":"https://arxiv.org/abs/2511.08427","url_pdf":"https://arxiv.org/pdf/2511.08427v1","authors":"[\"Linda-Sophie Schneider\",\"Yipeng Sun\",\"Chengze Ye\",\"Markus Michen\",\"Andreas Maier\"]","published":"2025-11-11T16:34:40Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":607224,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2843511,"paper_url":"https://arxiv.org/abs/2511.08427","paper_title":"An update to PYRO-NN: A Python Library for Differentiable CT Operators","repo_url":"https://github.com/csyben/PYRO-NN","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}