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多学科生物医学计算领域的独立和成功的研究人员，并在计算和生物医学科学方面拥有独特的知识和广泛的经验。爱荷华大学拥有根深蒂固的多学科文化，最近建立了影像引导放射治疗卓越中心，这是一个世界级的放射治疗设施。在这个优秀的环境中，在电气与计算机工程系和放射肿瘤学系联合任命的候选人将通过以下途径发展他的研究生涯：(1)学习放射治疗和医学成像课程；㈡在最相关的医学和计算机科学会议上介绍研究成果；（iii）参加本中心的研究小组会议和临床评估与规划会议。

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.