更新版PYRO-NN实现可微分CT重建，支持PyTorch与CUDA加速。
An update to PYRO-NN: A Python Library for Differentiable CT Operators
- 基于PyTorch构建可微分投影算子，兼容多种扫描几何。
- 原生CUDA支持提升平行/扇形/锥形束投影效率。
- 适合做端到端可训练重建的科研人员使用。
深度学习为现代X射线计算机断层成像（CT）重建带来了显著进展，解决了新兴成像技术带来的挑战。这些进展得益于将经典重建方法与数据驱动技术相结合。可微分算子在这一融合中起关键作用，使端到端优化成为可能，并能将物理建模融入神经网络。本文介绍更新版的PYRO-NN——一个用于可微分CT重建的Python库。新框架扩展了对PyTorch的支持，并引入原生CUDA内核，实现平行、扇形及锥形束几何下投影与反投影操作的高效计算。此外，该库还提供成像伪影模拟工具、任意采集轨迹建模能力，以及通过高层级Python API构建灵活端到端可训练流程的功能。代码已开源：https://github.com/csyben/PYRO-NN
原文摘要 · Abstract (English)
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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