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

arXiv: https://arxiv.org/abs/2511.08427 · HTML: https://mlsanity.com/paper/2511.08427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 11 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [TorchRadon: Fast Differentiable Routines for Computed Tomography](https://mlsanity.com/paper/2009.14788/) (2020)
- [PYRO-NN: Python Reconstruction Operators in Neural Networks](https://mlsanity.com/paper/1904.13342/) (2019)
- [Core Imaging Library -- Part I: a versatile Python framework for tomographic imaging](https://mlsanity.com/paper/2102.04560/) (2021)
- [Core Imaging Library -- Part II: Multichannel reconstruction for dynamic and spectral tomography](https://mlsanity.com/paper/2102.06126/) (2021)
- [On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting](https://mlsanity.com/paper/2211.01111/) (2022)
- [Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series](https://mlsanity.com/paper/2401.16039/) (2024)
- [Deep Learning Computed Tomography based on the Defrise and Clack Algorithm](https://mlsanity.com/paper/2403.00426/) (2024)
- [DRACO: Differentiable Reconstruction for Arbitrary CBCT Orbits](https://mlsanity.com/paper/2410.14900/) (2024)
- [EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction](https://mlsanity.com/paper/2403.10695/) (2024)
- [Auto-Calibration of Cone Beam Geometries from Arbitrary Rotating Markers using a Vector Geometry Formulation of Projection Matrices](https://mlsanity.com/paper/1808.03337/) (2018)
- [A 2D Sinogram-Based Approach to Defect Localization in Computed Tomography](https://mlsanity.com/paper/2401.16104/) (2024)

## Abstract (arXiv)

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