Accurate Target Delineation and Motion Tracking to Improve IMRT Effectiveness

描述(由申请人提供)：拟议的K25职业发展奖将允许候选人成为新兴的多学科生物医学计算领域的独立和成功的研究员，拥有一套独特的计算和生物医学科学方面的知识和丰富的经验。拥有深厚的多学科文化根基的爱荷华大学最近成立了图像引导放射治疗卓越中心，这是一家世界级的放射治疗机构。在这种良好的环境下，应聘者将通过(I)参加放射治疗和医学成像课程；(Ii)在最相关的医学和计算机科学会议上展示研究成果；(Iii)参加研究小组会议和中心的临床评估和规划会议，发展自己的研究事业。 候选人的研究项目是开发新的算法、方法和软件工具，利用计算机科学和医学成像的最新进展，进行准确的目标定义和运动跟踪，从而提高调强放射治疗(IMRT)的治疗效果。靶点勾画和局部器官运动是影响调强放射治疗效果的两个主要因素。通过将目标描述和运动跟踪问题描述为计算加权有向图中的最优闭集，实现了计算的可行性。我们的方法的新功能将继续专注于解决方案的全局最优性。我们假设先进的图形算法和几何技术能够准确地描绘目标和精确跟踪内部肿瘤/器官运动，从而提高调强放射治疗的效果。拟议研究的具体目标如下： 1)开发和验证体图像数据中单个和多个交互曲面的最佳描绘方法；具有地形形状、管状和封闭形状的曲面以及具有复杂拓扑的曲面将被包括在内。 2)开发和验证一种使用4-D图像数据跟踪治疗过程中最佳器官运动的方法。 3)开发和验证一种精确的组织体素映射方法，该方法能够将一个运动阶段的3D治疗计划转换到所有其他阶段，以形成4-D治疗计划。 公共卫生相关性：我们预计建议的方法的临床应用将对调强放疗的治疗效果产生可衡量的改善，导致更好的局部肿瘤控制和显著提高癌症生存率。

DESCRIPTION (provided by applicant): The proposed K25 career development award will allow the candidate to become an independent and successful researcher in the emerging multidisciplinary biomedical computing area armed with a unique set of knowledge and breadth of experience both in computational and biomedical sciences. The University of Iowa with a well-rooted multidisciplinary culture, has newly established the Center of Excellence in Image-Guided Radiation Therapy, a world-class radiation therapy facility. In this excellent environment, the candidate, having a joint appointment at Departments of Electrical & Computer Engineering and Radiation Oncology, will develop his research career through (i) taking courses in radiation therapy and medical imaging; (ii) presenting research results at the most pertinent medical and computer science conferences; (iii) attending research group meetings and clinical evaluation and planning meetings at the Center. The research project of the candidate is to develop novel algorithms, methods, and software tools that make use of the latest advances in computer science and medical imaging for accurate target definition and motion tracking, thus improving the treatment effectiveness of Intensity-Modulate Radiation Therapy (IMRT). Target delineation and intra-fraction organ motion are two major sources that compromise the treatment effectiveness of IMRT. The computational feasibility is accomplished by formulating the target delineation and motion tracking problems as computing an optimal closed set in a weighted directed graph. The novel features of our method will be designed with a continuing focus on the global optimality of the solution. We hypothesize that advanced graph algorithmic and geometric techniques enable accurate target delineation and precise tracking of internal tumor/organ motion, thus improving IMRT effectiveness. The specific aims of the proposed research are as follows: 1) Develop and validate a method for the optimal delineation of single and multiple interacting surfaces in volumetric image data; surfaces with terrain-like, tubular, and closed shapes as well as those with complex topologies will be included. 2) Develop and validate a method for tracking optimal organ motion over the treatment course using 4-D image data. 3) Develop and validate a method for accurate tissue voxel mapping which enables to transfer a 3-D treatment plan at one motion phase to all other phases to form a 4-D treatment plan. Public Health Relevance: We expect the clinical applications of the proposed approaches will have a measurable improvement on IMRT treatment effectiveness, leading to a better local tumor control and a significant increase of cancer survival rate.