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Generalized inverse optimization with application to radiation therapy

Generalized inverse optimization with application to radiation therapy
广义逆优化在放射治疗中的应用
批准号:
RGPIN-2015-05180
负责人:
Chan, Timothy
金额:
$2.04万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

项目摘要

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中文摘要
翻译
该研究项目将开发优化领域的新工具,并展示这些工具的应用,以开创放射治疗治疗计划的新范式。放射治疗是治疗癌症的主要方法之一。目前的治疗计划过程复杂且效率低下,通常依靠人工试错来制定治疗计划。挑战源于在规划软件中确定适当参数的困难,这些参数将导致可接受的处理。*在本研究项目中,我们将开发新的逆优化方法,可以分析历史处理和逆向工程适当的处理参数。然后,机器学习方法将被开发出来,从个体的解剖结构中预测这些治疗参数。我们新的基于知识的治疗计划框架,建立在优化和机器学习的基础上,将能够同时提高治疗计划过程的标准化,而不会牺牲治疗的个性化。****我们的研究将推动逆优化理论的前沿,并使这些新工具能够应用于放射治疗以外的各种领域。随着大数据的兴起,我们的逆优化方法将发现越来越多的适用性,并代表了一种从大数据集中获得新见解的新方法。分析和优化是一个快速发展的领域,毕业生供不应求。参与这项研究的学生将培养高度就业的技术技能,这些技能将在技术导向、数据驱动的行业中得到重视,包括医疗保健、交通运输、制造业、金融、供应链管理和能源
英文摘要
This research program will develop new tools in the field of optimization and demonstrate the application of those tools to pioneer a new paradigm in radiation therapy treatment planning. Radiation therapy is one of the primary ways to treat cancer. The current treatment planning process is complex and inefficient, typically relying on manual trial-and-error effort to produce a treatment plan. The challenge stems from the difficulty in determining appropriate parameters in the planning software that will result in an acceptable treatment.*In this research program, we will develop new inverse optimization methods that can analyze historical treatments and reverse-engineer appropriate treatment parameters. Then, machine learning methods will be developed to predict these treatment parameters from an individual's anatomy. Our new knowledge-based treatment planning framework, built on a foundation of optimization and machine learning, will be able to simultaneously improve standardization of the treatment planning process without sacrificing personalization of the treatments.****Our research will push forward the frontiers of the theory of inverse optimization and enable the application of these new tools to a wide variety of domains beyond radiation therapy. With the rise of big data, our inverse optimization methods will find increasing applicability and represent a new way to derive novel insight from large datasets. Analytics and optimization is a fast-growing field that is in short supply of graduates. Students who participate in this research will develop highly employable technical skills that will be valued in technology-oriented, data-driven industries including healthcare, transportation, manufacturing, finance, supply chain management, and energy.***
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Novel Optimization and Analytics in Health
  • 批准号:
    CRC-2018-00310
  • 项目类别:
    Canada Research Chairs
  • 资助金额:
    $7.29万
  • 财政年份:
    2022
  • 负责人:
    Chan, Timothy
  • 依托单位:
Automated decision making via optimization and machine learning
  • 批准号:
    RGPIN-2020-04082
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $3.79万
  • 财政年份:
    2022
  • 负责人:
    Chan, Timothy
  • 依托单位:
Automated decision making via optimization and machine learning
  • 批准号:
    DGDND-2020-04082
  • 项目类别:
    DND/NSERC Discovery Grant Supplement
  • 资助金额:
    $2.91万
  • 财政年份:
    2022
  • 负责人:
    Chan, Timothy
  • 依托单位:
Novel Optimization And Analytics In Health
  • 批准号:
    CRC-2018-00310
  • 项目类别:
    Canada Research Chairs
  • 资助金额:
    $7.29万
  • 财政年份:
    2021
  • 负责人:
    Chan, Timothy
  • 依托单位:
国内基金
海外基金
新型简化Inverse Lax-Wendroff方法的发展与应用
  • 批准号:
    --
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    30万元
  • 批准年份:
    2022
  • 负责人:
    程自强
  • 依托单位:
基于高阶格式的Inverse Lax-Wendroff方法及其稳定性分析
  • 批准号:
    11801143
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    25.0万元
  • 批准年份:
    2018
  • 负责人:
    李婷婷
  • 依托单位: