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

Schneider LS, Sun Y, Ye C, Michen M, Maier A (2026)

Publication Status: Submitted

Publication Type: Unpublished / Preprint

Future Publication Type: Journal article

Publication year: 2026

Publisher: arXiv

DOI: 10.48550/arXiv.2511.08427

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.

Authors with CRIS profile

Linda-Sophie Schneider Lehrstuhl für Informatik 5 (Mustererkennung) Yipeng Sun Lehrstuhl für Informatik 5 (Mustererkennung) Chengze Ye Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME)

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Schneider, L.-S., Sun, Y., Ye, C., Michen, M., & Maier, A. (2026). An update to PYRO-NN: A Python Library for Differentiable CT Operators. (Unpublished, Submitted).

MLA:

Schneider, Linda-Sophie, et al. An update to PYRO-NN: A Python Library for Differentiable CT Operators. Unpublished, Submitted. 2026.

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