Collaborative Research: DDDAS-TMRP: Dynamic Data-Driven System for Laser Treatment of Cancer

合作研究：DDDAS-TMRP：用于癌症激光治疗的动态数据驱动系统

• 批准号: 0540033
• 负责人: J. Tinsley Oden
• 金额: --
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2009-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540033&HistoricalAwards=false
• 关键词: Collaborative; Research; DDDAS; TMRP; Dynamic

The goal of this project is to develop a dynamic data-driven planning and controlsystem for laser treatment of cancer. The proposed research includes (1) development of a general mathematical framework and a family of mathematical and computational models of bio-heat transfer, tissue damage, and tumor viability, (2) dynamic calibration, verification and validation processes based on laboratory and clinical data and simulated response, and (3) design of effective thermo-therapeutic protocols using model predictions. At the core of the proposed systems is the adaptive-feedback control of mathematical and computational models based on a posteriori estimates of errors in key quantities of interest, and modern Magnetic Resonance Temperature Imaging (MRTI) and diode laser devices to monitor treatment of tumors in laboratory animals. This approach enables an automated systematic model selection process based on acceptance criteria determined a priori and is valid for models of events occurring at multiple spatial and temporal scales. The proposed project should be of interest to both NIH/NLM and NSF. The methodologies to be implemented involve uncertainty quantification methods designed to provide an innovative, data-driven, patient-specific approach to effective cancer treatment. The general mathematical framework resulting from this research will be applicable to any thermo-therapeutic cancer treatment, but our treatment protocols will be established based on tumors seeded in prostates of canines. The primary objective of the proposed research is to develop treatment strategies by selecting optimal parameter sets (such as laser power, wave length, and fluence rate) based on high fidelity model predictions and data from cellular and in vivo biological measurement, and MRTI thermal distributions.

本项目的目标是开发一个用于激光治疗癌症的动态数据驱动的计划和控制系统。建议的研究包括(1)建立生物热传递、组织损伤和肿瘤存活的通用数学框架和一系列数学和计算模型，(2)基于实验室和临床数据以及模拟反应的动态校准、验证和验证过程，以及(3)利用模型预测设计有效的热疗方案。所提出的系统的核心是数学和计算模型的自适应反馈控制，该控制基于对感兴趣的关键数量的误差的后验估计，以及用于监测实验室动物肿瘤治疗的现代磁共振温度成像(MRTI)和半导体激光设备。该方法实现了基于先验确定的接受标准的自动化系统模型选择过程，并且对在多个空间和时间尺度上发生的事件的模型有效。NIH/NLM和NSF都应该对拟议的项目感兴趣。将要实施的方法包括不确定性量化方法，旨在提供一种创新的、数据驱动的、针对患者的有效癌症治疗方法。这项研究得出的一般数学框架将适用于任何热疗癌症治疗，但我们的治疗方案将建立在犬前列腺种植的肿瘤基础上。拟议研究的主要目标是根据高保真模型预测、细胞和体内生物测量数据以及MRTI热分布，通过选择最佳参数集(如激光功率、波长和注量率)来制定治疗策略。