Accurate Target Delineation and Motion Tracking to Improve IMRT Effectiveness

拟议的K25职业发展奖将使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人员，拥有计算和生物医学科学方面的独特知识和丰富经验。爱荷华州大学拥有根深蒂固的多学科文化，新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。在这种良好的环境中，候选人，在电气与计算机工程和放射肿瘤学部门的联合任命，将通过（i）参加放射治疗和医学成像课程发展他的研究生涯;（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临床评估和规划会议在中心。 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。靶区勾画和分次内器官运动是影响调强放射治疗效果的两个主要因素。通过将目标描绘和运动跟踪问题公式化为计算加权有向图中的最优闭集来实现计算可行性。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，使准确的目标划定和内部肿瘤/器官运动的精确跟踪，从而提高调强放射治疗的有效性。拟议研究的具体目标如下： 1)开发并验证一种方法，用于在体积图像数据中最佳描绘单个和多个相互作用的表面;将包括具有地形、管状和封闭形状以及具有复杂拓扑结构的表面。 2)开发并验证使用4-D图像数据跟踪治疗过程中最佳器官运动的方法。 3)开发并确认一种用于精确组织体素映射的方法，该方法能够将一个运动阶段的3-D治疗计划转移到所有其他阶段，以形成4-D治疗计划。 公共卫生相关性：我们期望所提出的方法的临床应用将有一个可衡量的改善调强放射治疗的有效性，导致更好的局部肿瘤控制和显着增加癌症的生存率。

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.