Intensity Modulated Radiation Therapy: Integrated Models and Algorithms

这笔赠款支持为调强放射治疗(IMRT)治疗计划开发新的综合模型和算法。调强放射治疗被认为是治疗多种癌症的最有效的放射疗法。现有的方法将治疗计划问题分解为几个阶段，并依次求解相应的子问题。尽管这可能导致较差的治疗计划，但到目前为止，由于缺乏适当的模型配方来提供有效的解决方法，阻碍了对子问题的整合。特别是，最受关注的子问题是确定用于照射癌症患者的每个光束的强度分布或通量图的中心问题。然而，众所周知，与治疗传递效率和质量有关的问题对调强放疗治疗有重大影响。将开发充分考虑到治疗设备的潜力和局限性以及患者在治疗过程中解剖结构变化的模型，以及解决由此产生的综合问题的算法。所有开发的模型和算法都将在实际的临床患者数据上进行测试。每年有数十万患者接受放射治疗。该项目将为调强放射治疗计划问题提供高效和强大的综合模型，将目前尚未令人满意地解决的临床考虑纳入其中。开发有效的方法来在现实的计算时间内解决由此产生的综合优化问题，将为医生和临床医生提供实时决策支持，并将帮助他们找到不需要在治愈或控制疾病的好机会和潜在的生活质量之间做出选择的治疗方案。

This grant supports the development of new integrated models and algorithms for intensity modulated radiation therapy (IMRT) treatment planning. IMRT is considered to be the most effective radiation therapy for many forms of cancer. Current approaches decompose the treatment planning problem into several stages and the corresponding subproblems are solved sequentially. Although this may lead to inferior treatment plans, the lack of suitable model formulations that allow for efficient solution methods has so far prevented the integration of the subproblems. In particular, the subproblem that has received the most attention is the central problem of determining an intensity profile or fluence map for each of the beams used in irradiating a cancer patient. However, issues pertaining to treatment-delivery efficiency and quality are known to have a significant impact on IMRT treatments. Models that fully take into account both potential and limitations of the treatment equipment as well as changes in the patient anatomy over the course of the treatment will be developed along with algorithms to solve the resulting integrated problems. All of the developed models and algorithms will be tested on actual clinical patient data.Every year, hundreds of thousands of patients receive cancer treatment by radiation therapy. This project will provide highly effective and robust integrated models for IMRT treatment-planning problems that integrate clinical considerations that are currently not yet addressed satisfactorily. The development of efficient approaches to solve the resulting integrated optimization problems in realistic computational times will provide real-time decision support to physicians and clinicians and will help them to find treatment plans that do not require a choice between a good chance for a cure or control of disease and the potential quality of life.