Computed Tomography (CT) has been widely adopted in medicine and it is increasingly being used in scientific and industrial applications. Parallelly, research in different mathematical areas concerning discrete inverse problems has led to the development of new sophisticated numerical solvers that can be applied in the context of CT. The Tomographic Iterative GPU-based Reconstruction (TIGRE) toolbox was born almost a decade ago precisely in the gap between mathematics and high performance computing for real CT data, providing user-friendly open-source software tools for image reconstruction. However, since its inception, the tools' features and codebase have had over a twenty-fold increase, and are now including greater geometric flexibility, a variety of modern algorithms for image reconstruction, high-performance computing features and support for other CT modalities, like proton CT. The purpose of this work is two-fold: first, it provides a structured overview of the current version of the TIGRE toolbox, providing appropriate descriptions and references, and serving as a comprehensive and peer-reviewed guide for the user; second, it is an opportunity to illustrate the performance of several of the available solvers showcasing real CT acquisitions, which are typically not be openly available to algorithm developers.

翻译：计算机断层扫描（CT）已在医学领域得到广泛应用，并日益普及于科学与工业领域。与此同时，离散反问题相关数学领域的研究推动了新型复杂数值求解器的发展，这些求解器可应用于CT场景。基于GPU的迭代断层扫描重建（TIGRE）工具箱诞生于近十年前，其初衷正是为真实CT数据搭建数学与高性能计算之间的桥梁，为用户提供友好的开源图像重建软件工具。然而自发布以来，该工具的功能特性与代码库规模已增长逾二十倍，目前涵盖更强大的几何灵活性、多种现代图像重建算法、高性能计算特性以及对质子CT等其他CT模态的支持。本研究具有双重目的：首先，系统梳理当前版本TIGRE工具箱的功能架构，提供规范的描述与参考文献，为用户提供经同行评审的综合性使用指南；其次，通过展示典型算法开发者难以获取的真实CT采集数据，直观呈现多种现有求解器的实际性能表现。