Online Collection of Patient-Specific Information for Daily Prostate Segmentation

描述(申请人提供)：在影像引导放射治疗期间，准确的前列腺分割对于补偿每日的前列腺运动非常重要。这对于适应性放射治疗也很重要，以便最大限度地增加肿瘤的剂量，最大限度地减少对健康组织的剂量。该项目的目标是开发一种新的方法，用于在线学习患者特定的外观和形状变形信息，以显著改善从日常CT图像中分割出的前列腺。我们的前两个具体目标集中在开发一种在线学习方法，从随后获得的同一患者的治疗图像中逐步建立患者特定的外观和形状变形模型，以指导更准确的前列腺分割。基于人群的外观和形状变形模型并不是研究中的患者所特有的，因此它们仅用于早期治疗的前列腺分割。一旦从足够数量的治疗图像中在线收集了特定于患者的信息，它就开始在分割过程中取代基于人群的信息。此外，通过创新性地将这些方法中的外观匹配问题表述为一个新的配准问题，有效地解决了传统基于模型的方法需要强点对点对应的局限性，从而显著提高了前列腺分割的灵活性和最终的准确性。我们的第三个具体目标是通过在线学习前列腺边界和内部区域变形之间的特定关联，快速将分割的前列腺配准到患者的计划图像和每个治疗图像中。这将允许将治疗计划从计划图像空间快速翘曲到用于自适应放射治疗的治疗图像空间，并且还将允许对放射治疗进行剂量学评估。我们的第四个具体目标是使用物理模型和真实的患者数据来评估所提出的前列腺分割和配准算法，并将其性能与现有的前列腺分割算法进行比较。随着这些潜在更准确的分割和快速配准方法的成功开发，放射治疗癌症治疗的有效性将得到极大的提高。为了使研究界受益，该项目中最终开发的方法也将被纳入PLanUNC，这是一个由北卡罗来纳大学开发的功能齐全、文件充分、开放源码的治疗规划系统，并将免费向公众提供。 公共卫生相关性：该项目旨在开发一种在线学习患者特定外观和形状变形信息的新方法，作为一种显著改善图像引导放射治疗期间患者日常CT图像的前列腺分割和配准的方法。最终开发的方法一旦得到验证，将被纳入PLanUNC，这是北卡罗来纳大学开发的一个功能齐全、文件充分、开放源码的治疗规划系统，并将免费向公众提供。

DESCRIPTION (provided by applicant): Accurate segmentation of the prostate is important in compensating for daily prostate motion during image- guided radiation therapy. It is also important for adaptive radiation therapy in order to maximize dose to the tumor and minimize dose to healthy tissue. The goal of this project is to develop a novel method for online learning of patient-specific appearance and shape deformation information to significantly improve prostate segmentation from daily CT images. Our first two specific aims focus on developing an online-learning method for progressively building the patient-specific appearance and shape deformation models from the subsequently acquired treatment images of the same patient, to guide more accurate segmentation of the prostate. The population-based appearance and shape deformation models are not specific to the patient under study, and therefore they are used only for prostate segmentation in early treatment days. Once patient-specific information has been collected online from a sufficient number of treatment images, it starts to replace the population-based information in the segmentation process. In addition, the limitation of requiring strong point-to-point correspondence in the conventional model-based methods will be effectively solved by innovatively formulating the appearance matching in these methods as a new registration problem, thus significantly improving the flexibility and eventually the accuracy of prostate segmentation. Our third specific aim is to rapidly register the segmented prostates in the planning image and each treatment image of a patient, by online learning the patient-specific correlations between the deformations of prostate boundaries and internal regions. This will allow for fast warping of the treatment plan from the planning image space to the treatment image space for adaptive radiotherapy, and will also allow for the dosimetric evaluation of radiotherapy. Our fourth specific aim is to evaluate the proposed prostate segmentation and registration algorithms by using both physical phantom and real patient data, and to compare its performance with existing prostate segmentation algorithms. With successful development of these potentially more accurate segmentation and fast registration methods, the effectiveness of radiotherapy for cancer treatment will be highly improved. To benefit the research community, the final developed method in this project will also be incorporated into PLanUNC, a full- featured, fully documented, open-source treatment planning system developed at UNC, and will be made freely available to the public. PUBLIC HEALTH RELEVANCE: This project aims at developing a novel method for online learning of patient-specific appearance and shape deformation information, as a way to significantly improve prostate segmentation and registration from daily CT images of a patient during image- guided radiation therapy. The final developed methods, once validated, will be incorporated into PLanUNC, a full- featured, fully documented, open-source treatment planning system developed at UNC, and will be made freely available to the public.