High-Throughput Computing for a Multi-Plan Framework in Radiotherapy

放射治疗多计划框架的高吞吐量计算

• 批准号: 7736445
• 负责人: WARREN D D'SOUZA
• 金额: $ 31.74万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7736445
• 关键词: Accounting; Algorithms; Behavior; Clinical Engineering; Collection; Complex; Complication; Computational Science; Data; Decision Support Systems; Dependence; Development; Dose; Engineering; Environment; External Beam Radiation Therapy; Generations; Genetic Programming; Goals; Intensity-Modulated Radiotherapy; Knowledge; Lead; Linear Accelerator Radiotherapy Systems; Linear Programming; Machine Learning; Maps; Medical; Methods; Modeling; Monte Carlo Method; NIH Program Announcements; Organ; Patients; Physicians; Physics; Process; Property; Radiation; Radiation Oncology; Radiation therapy; Relative (related person); Research Personnel; Risk; Sampling; Shapes; Simulate; Solutions; Speed; Structure; Surface; System; Technology; Time; Toxic effect; base; cluster computing; combinatorial; computer science; computerized; computing resources; direct application; graphical user interface; heuristics; improved; innovation; insight; novel; novel strategies; predictive modeling; process optimization; programs; public health relevance; research clinical testing; tool; treatment planning; tumor

DESCRIPTION (provided by applicant): Computerized planning for radiation delivery via either external beam radiation therapy (EBRT) or intensity- modulated radiation therapy (IMRT) from linear accelerators is a complex process involving a large amount of input data and vast numbers of decision variables. Such large-scale combinatorial optimization problems are typically intractable for conventional approaches such as the direct application of the best available commercial algorithms, and thus specialized methods that take advantage of problem structure are required. Radiation treatment planning (RTP) problems are further complicated by the fact that they are multi-objective, that is, the RTP optimization process must take into account a trade-off between the competing goals of delivering appropriate doses to the tumor and avoiding the delivery of harmful radiation to nearby healthy organs. The goal of this proposal is to harness distributive computing via the Condor system for High Throughput Computing (HTC) within an RTP environment. The specific aims for this proposal are: 1) To develop a Nested Partitions (NP) framework that guides a global search process for optimal IMRT delivery parameters using HTC. 2) To develop parallel HTC-based linear programming (LP) methods to efficiently solve the dose optimization problem in IMRT for each given set of beam angles or beam apertures. (3) To exploit a high-throughput computing (HTC) environment and the developed NP/LP/segmentation framework to efficiently generate multiple plans for each given patient case. (4) To couple this multi-plan framework with a decision support system (DSS) that includes planning surface models, a graphical-user-interface (GUI) and machine learning tools to prediction OAR complication in order to aid in the ranking and selection of the generated treatment plans. This proposal requires a multi-disciplinary approach that is best conducted within the framework of the Innovations in Biomedical Computational Science and Technology program announcement. It brings together an interdisciplinary team of investigators with expertise in medical physics, mathematical programming, industrial engineering and clinical radiation oncology that is crucial to the development of the proposed multi- plan framework using HTC in radiation therapy. PUBLIC HEALTH RELEVANCE: The goal of this proposal is to develop a multi-dimensional platform for sophisticated treatment planning of radiation delivery. It will develop novel algorithms that will enable generation of superior treatment plans with the added advantage of increasing the speed of treatment planning. Further, it will allow physicians to know beforehand the quality of the treatment plan relative to the multiple treatment objectives and be able to determine the treatment complication scenario beforehand.

描述（由申请人提供）： 通过外部梁辐射疗法（EBRT）或线性加速器的强度调制辐射疗法（IMRT）进行辐射的计算机计划是一个复杂的过程，涉及大量输入数据和大量决策变量。这种大规模的组合优化问题通常对于常规方法（例如直接应用最佳的可用商业算法）来说是很棘手的，因此需要使用利用问题结构的专门方法。辐射治疗计划（RTP）问题是由于它们具有多目标的事实，即RTP优化过程必须考虑到将适当的剂量传递给肿瘤的竞争目标与避免向附近健康器官提供有害辐射的竞争目标之间的权衡。该提案的目的是通过Condor系统在RTP环境中利用高吞吐量计算（HTC）进行分配计算。该建议的具体目的是：1）开发一个嵌套分区（NP）框架，该框架使用HTC指导全局搜索过程，以实现最佳IMRT交付参数。 2）开发基于HTC的平行线性编程（LP）方法，以有效地为IMRT中的每组束角或梁孔径在IMRT中解决剂量优化问题。 （3）利用高通量计算（HTC）环境和开发的NP/LP/分割框架，以有效地为每个给定的患者案例生成多个计划。 （4）将这个多计划框架与决策支持系统（DSS）搭配，其中包括规划表面模型，图形 - 用户接口（GUI）和机器学习工具，以预测OAR并发症，以帮助制定生成的治疗计划的排名和选择。该建议需要一种多学科的方法，该方法最好在生物医学计算科学技术计划公告的创新框架内进行。它汇集了一个研究人员的跨学科团队，该团队在医学物理学，数学编程，工业工程和临床放射肿瘤学方面具有专业知识，这对于使用HTC使用HTC进行放射治疗的拟议多计划框架至关重要。公共卫生相关性：该提案的目的是开发一个多维平台，用于辐射提供的复杂治疗计划。它将开发出新颖的算法，该算法将能够生成卓越的治疗计划，并提高治疗速度。此外，这将使医生可以事先了解相对于多个治疗目标的治疗计划的质量，并能够事先确定治疗并发症情况。