论文标题
Torchradon：计算机断层扫描的快速可区分程序
TorchRadon: Fast Differentiable Routines for Computed Tomography
论文作者
论文摘要
这项工作介绍了Torchradon-一个开源CUDA库，其中包含一组解决计算机断层扫描（CT）重建问题的可区分例程。该图书馆旨在帮助研究CT问题的研究人员结合深度学习和基于模型的方法。该软件包是作为Pytorch扩展程序开发的，可以将其无缝集成到现有的深度学习培训代码中。与现有的Astra工具箱相比，Torchradon的速度更快125。 Torchradon实施的操作员允许使用Pytorch向后（）计算梯度，因此可以轻松地插入现有的神经网络体系结构中。由于其速度和GPU支持，Torchradon也可以有效地用作实施迭代算法的快速后端。本文介绍了图书馆的主要功能，将结果与现有库进行了比较，并提供了使用的示例。
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.
