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.

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