This work presents TorchRadon -- an open source CUDA library which contains a
set of differentiable routines for solving computed tomography (CT)
reconstruction problems. The library is designed to help researchers working on
CT problems to combine deep learning and model-based approaches. The package is
developed as a PyTorch extension and can be seamlessly integrated into existing
deep learning training code. Compared to the existing Astra Toolbox, TorchRadon
is up to 125 faster. The operators implemented by TorchRadon allow the
computation of gradients using PyTorch backward(), and can therefore be easily
inserted inside existing neural networks architectures. Because of its speed
and GPU support, TorchRadon can also be effectively used as a fast backend for
the implementation of iterative algorithms. This paper presents the main
functionalities of the library, compares results with existing libraries and
provides examples of usage.

本文介绍了 TorchRadon 库，它是一个基于 PyTorch 的 CUDA 开源库，提供了一些可微分的 CT 重建算法，并支持与深度学习算法的结合。相较于 Astra 工具箱，TorchRadon 速度快 125 倍，可以很容易地嵌入现有的神经网络中。同时，TorchRadon 也可以作为快速迭代算法的底层实现。