[论文解读] OpenTPS -- Open-source treatment planning system for research in proton therapy

S. Wuyckens, D. Dasnoy+10 more|arXiv (Cornell University)|Mar 1, 2023

Radiation Therapy and Dosimetry被引用 9

一句话总结

OpenTPS 是一款开源的、基于 Python 的质子治疗研究治疗计划系统，具灵活的计划创建、剂量计算和鲁棒优化能力，且由社区推动开发。

ABSTRACT

Introduction. Treatment planning systems (TPS) are an essential component for simulating and optimizing a radiation therapy treatment before administering it to the patient. It ensures that the tumor is well covered and the dose to the healthy tissues is minimized. However, the TPS provided by commercial companies often come with a large panel of tools, each implemented in the form of a black-box making it difficult for researchers to use them for implementing and testing new ideas. To address this issue, we have developed an open-source TPS. Approach. We have developed an open-source software platform, OpenTPS (opentps.org), to generate treatment plans for external beam radiation therapy, and in particular for proton therapy. It is designed to be a flexible and user-friendly platform (coded with the freely usable Python language) that can be used by medical physicists, radiation oncologists, and other members of the radiation therapy community to create customized treatment plans for educational and research purposes. Result. OpenTPS includes a range of tools and features that can be used to analyze patient anatomy, simulate the delivery of the radiation beam, and optimize the treatment plan to achieve the desired dose distribution. It can be used to create treatment plans for a variety of cancer types and was designed to be extended to other treatment modalities. Significance. A new open-source treatment planning system has been built for research in proton therapy. Its flexibility allows an easy integration of new techniques and customization of treatment plans. It is freely available for use and is regularly updated and supported by a community of users and developers who contribute to the ongoing development and improvement of the software.

研究动机与目标

• 通过提供可公开获取的 TPS 平台来推动并支持质子治疗的研究。
• 提供一个灵活、可扩展的软件体系结构（Core 与 GUI），用于数据管理、剂量计算、计划与评估。
• 促进在共享框架内整合新的剂量引擎、优化方法与鲁棒性概念。
• 通过数据增强和 4D 成像工具支持教育用途与 AI/机器学习研究。

提出的方法

• 建立两包架构：用于数据处理、剂量计算与计划的 Core 库；用于可视化与交互的 GUI。
• 通过 MCsquare 集成快速蒙特卡洛剂量计算，以计算光束分割剂量和剂量分布。
• 使用体素级剂量建模与光束矩阵 A，实施针对 IMPT 的计划设计与优化。
• 提供多种优化求解器（基于梯度、拟牛顿、FISTA、LP、内部点法，并可选使用 Gurobi）。
• 结合跨多种设定与范围不确定性的最坏情景评估，纳入鲁棒优化。
• 支持鲁棒性与 4D 剂量模拟（4DCT、MidP、变形场）及分割内/分割间运动分析。
• 包含数据增强工具，以模拟 AI/数据生成所需的分次内外运动。

Figure 1: Main window of the OpenTPS GUI.

实验结果

研究问题

• RQ1 开放源代码的 TPS 如何支持质子治疗领域的灵活、研究导向开发？
• RQ2 在开放框架内，哪些优化策略和求解器对 IMPT 计划优化最有效？
• RQ3 在研究型 TPS 中，如何纳入并评估对设定、剂量范围与运动不确定性的鲁棒性？
• RQ4 如何将 4D 剂量与运动模型整合用于计划评估和数据生成？
• RQ5 开放平台是否能加速新模态（如弧形治疗）和 AI 辅助计划的整合？

主要发现

• OpenTPS 提供了模块化的 Core/GUI 架构，具备数据处理、剂量计算（MCsquare）和计划优化能力。
• 支持多种求解器（基于梯度、FISTA、LBFGS、LP、内部点法）以及基于 Gurobi 的线性模型优化。
• 实现并可操作的跨 21 种情景（7 设置 x 3 范围）的最坏情况鲁棒优化。
• 支持 4D 剂量模拟（4DD 与 4D）和 4D 运动建模，以分析运动相关的剂量退化。
• 该平台包含广泛的数据增强工具（分次内外运动），用于 AI 与方法开发的训练/测试数据生成。

Figure 2: The tumor motion is schematically represented by the hysteresis formed by the tumor position (gray circles) at each phase of the 4DCT (a). Deformation fields (blue vectors) are generated using registration between the first phase (P1) and all others phases (b). Then, all phases can be defo