Online Collection of Patient-Specific Information for Daily Prostate Segmentation

描述（由申请人提供）：前列腺的准确分割对于补偿图像引导放射治疗期间的日常前列腺运动非常重要。它对于适应性放射治疗也很重要，以便最大化肿瘤剂量并最小化健康组织剂量。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，以显着改善日常CT图像中的前列腺分割。 我们的前两个具体目标专注于开发一种在线学习方法，用于从随后采集的同一患者的治疗图像中逐步构建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。基于群体的外观和形状变形模型并不特定于研究中的患者，因此它们仅用于早期治疗日的前列腺分割。一旦从足够数量的治疗图像中在线收集了患者特定信息，它就开始在分割过程中替换基于人群的信息。此外，在传统的基于模型的方法中，需要强的点对点对应的限制将有效地解决通过创新地制定这些方法中的外观匹配作为一个新的配准问题，从而显着提高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患者特定相关性，快速配准患者的规划图像和每个治疗图像中的分割前列腺。这将允许治疗计划从计划图像空间到治疗图像空间的快速扭曲以用于自适应放射疗法，并且还将允许放射疗法的剂量测定评估。我们的第四个具体目标是通过使用物理体模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。 随着这些潜在的更准确的分割和快速配准方法的成功开发，放射治疗癌症的有效性将大大提高。为了使研究界受益，该项目中最终开发的方法也将被纳入PLANELY，这是一个功能齐全、文件齐全的开源治疗计划系统，将免费提供给公众。 公共卫生相关性：该项目旨在开发一种用于在线学习患者特定外观和形状变形信息的新方法，作为在图像引导放射治疗期间显著改善患者日常CT图像的前列腺分割和配准的一种方式。最终开发的方法一旦得到验证，将被纳入PLANELY，这是一个功能齐全、文件齐全、开源的治疗计划系统，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.