Development of AI-Augmented quality assurance tools for radiation therapy

人工智能增强的放射治疗质量保证工具的开发 项目总结 质量保证(QA)是放射治疗(RT)工作流程的重要组成部分，它决定了 病人护理的成功。然而，目前的治疗计划质量保证方法和工具在以下方面存在不足 多方面的，存在问责有限、劳动强度大、成本高等问题。在……里面 在这个项目中，我们将利用新兴的深度学习技术来创建临床可翻译的 现代RT稳健高效的质量保证解决方案。具体地说，我们的目标是(I)建立一部小说 使用深度学习来验证RT治疗计划的机器交付参数的框架；(Ii) 调查深度学习用于RT剂量验证的使用情况；以及(Iii)评估QA的性能 系统并显示其潜在的临床影响 。这项研究提出了首个此类治疗方案。 能够提供机器交付参数(MLC孔径和MU)的QA策略 涉及调强放射治疗/VMAT野)和患者治疗几何形状的剂量学分布。这个 研究还将使我们有可能利用这两种深度学习模型的有用特征 并检查治疗计划的周期一致性(即，从 从平面到相应的3D剂量分布，再从3D剂量到束流参数) 以获得增强型计划QA。该项目的成功完成将提供急需的计划质量保证工具 安全、高效、高质量的RT实践，使患者真正受益于现代RT 医疗模式。最后，建议的策略是相当广泛的，可以很容易地推广到其他 治疗方式，如质子治疗和高剂量率(HDR)近距离放射治疗。

Development of AI-Augmented quality assurance tools for radiation therapy Project summary Quality assurance (QA) is an essential part of radiation therapy (RT) workflow and critically determines the success of patient care. However, current treatment plan QA methods and tools are deficient in multiple aspects and suffer from problems such as limited accountability, labor intensive, and costly. In this project we will leverage the emerging deep learning techniques to create clinically translatable solutions for robust and efficient QA of modern RT. Specifically, we aim to (i) establish a novel framework for using deep learning to verify the machine delivery parameters of an RT treatment plan; (ii) investigate the use of deep learning for RT dose verification; and (iii) evaluate the performance of the QA system and show its potential clinical impact . This research presents the first-of-its-kind treatment plan QA strategy capable of providing both machine delivery parameters (MLC apertures and MUs of the involved IMRT/VMAT fields) and dosimetric distribution on the patient’s treatment geometry. The research will also make it possible to take advantage of the useful features of both deep learning models from Aims 1 and 2 and check the cycle-consistency of a treatment plan (i.e., from the beam parameters of the plan to the corresponding 3D dose distribution, and then from the 3D dose to the beam parameters) for enhanced plan QA. Successful completion of the project will provide urgently needed plan QA tools for safe, efficient and high-quality RT practice, and enable patients to truly benefit from modern RT modalities. Finally, the proposed strategy is quite broad and can be readily generalized for QA of other treatment modalities, such as proton therapy and high-dose rate (HDR) brachytherapy.