Development of AI-Augmented quality assurance tools for radiation therapy

开发用于放射治疗的人工智能增强质量保证工具

• 批准号: 10558155
• 负责人: Lei Xing
• 金额: $ 55.01万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10558155
• 关键词: 3-Dimensional; Accountability; Architecture; Archives; Back; Brachytherapy; Clinical; Collimator; Communities; Consumption; Data; Dependence; Development; Dose; Evaluation; Geometry; High-Dose Rate Brachytherapy; Image; Intensity-Modulated Radiotherapy; Learning; Machine Learning; Maps; Measurement; Methods; Modality; Modeling; Modernization; Monitor; Output; Patient Care; Patient Education; Patients; Performance; Plant Leaves; Positioning Attribute; Radiation Oncology; Radiation therapy; Records; Research; Residual state; Safety; Scheme; Shapes; System; Techniques; Testing; Time; Training; Uncertainty; Validation; X-Ray Computed Tomography; clinical translation; cost; deep learning; deep learning model; design; improved; indexing; learning strategy; novel; proton therapy; quality assurance; success; tool; treatment planning; treatment strategy

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.

开发用于放射治疗的AI增强质量保证工具 项目摘要 质量保证（QA）是放射治疗（RT）工作流程的重要组成部分， 病人护理的成功。然而，当前的治疗计划QA方法和工具在以下方面存在缺陷： 多个方面，并遭受诸如有限责任、劳动密集和成本高的问题。在 在这个项目中，我们将利用新兴的深度学习技术， 现代RT的强大和有效的QA解决方案。具体来说，我们的目标是（i）建立一个新的 使用深度学习验证RT治疗计划的机器输送参数的框架;（ii） 研究深度学习在RT剂量验证中的应用;以及（iii）评估QA的性能 系统，并显示其潜在的临床影响 .这项研究提出了第一种治疗方案， 能够提供机器输送参数（MLC孔径和 涉及IMRT/VMAT场）和患者治疗几何结构上的剂量分布。的 研究还将使利用这两种深度学习模型的有用功能成为可能 并检查治疗计划的周期一致性（即，从光束参数 计划到相应的3D剂量分布，然后从3D剂量到射束参数） 增强计划质量保证。该项目的成功完成将提供急需的计划质量保证工具 安全、高效和高质量的RT实践，使患者真正受益于现代RT 方式。最后，所提出的策略是相当广泛的，可以很容易地推广到其他QA 治疗方式，如质子治疗和高剂量率（HDR）近距离放射治疗。