AUTOMATIC 3D REGISTRATION FOR ENHANCED CANCER MANAGEMENT

描述（由申请人提供） 在技术发展的推动下，肿瘤科医生/放射科医生目前 在越来越多的更高分辨率的解剖和 功能成像数据。GE的新型LightSpeed CT扫描仪只是其中之一 此类新采集设备的典型示例。当务之急是， 开发图像处理工具，以帮助专家过滤 大量的数据，以保持甚至提高其灵敏度 和检测肿瘤变化的特异性。我们相信这笔拨款 是朝着这一重要方向迈出的重要一步， 一个真正的多学科团队的努力。 用于增强癌症管理的自动3D配准是一项提案， 支持有限的，临床前测试的能力，先进的图像 处理技术，以改善管理的癌症， 从现有诊断中为临床医生提供更多更早的信息 方式。该计划项目资助包括a）使用fMRI进行预处理 （神经）手术计划侧重于运动障碍患者 与运动带附近的病变有关， 对于当前fMRI技术来说足够不动，B）支持损伤 重复、间隔CT和超声的变化检测和量化 检查，以便监测并可能根据以下情况修改治疗计划 个体患者响应，c）在辐射中定义器官的自动化 使用非造影CT数据集的治疗计划，以及d） 开发一种使用配准CT和PET的方法， 只有胸部CT检查才显示有阳性淋巴结

DESCRIPTION (provided by applicant) Driven by technology development, the oncologist/radiologist is currently under assault by increasing quantities of higher resolution anatomical and functional imaging data. The new LightSpeed CT scanner by GE is but one typical example of such new acquisition devices. It is imperative that new image processing tools be developed to help specialists filter through the massive amounts of data in order to maintain or even improve their sensitivity and specificity in detecting neoplastic changes. We believe that this grant is a significant step in this important direction and represents the best efforts of a truly multidisciplinary team. Automatic 3D Registration for Enhanced Cancer Management is a proposal to support limited, preclinical testing of the ability of advanced image processing techniques to improve the management of cancer by routinely culling more and earlier information for the clinician from existing diagnostic modalities. This program project grant includes a)the use of fMRI for pre (neuro)surgical planning focused on patients with movement disorders associated with lesions near the motor strip who could never remain sufficiently motionless for current fMRI techniques, b) support for lesion change detection and quantification from repeated, interval CT and ultrasound exams in order to monitor and potentially modify therapy plans based on individual patient response, c) automation in defining organs in radiation therapy treatment planning using noncontrast CT data sets, and d) the development of a method using registered CT and PET for reducing false positive nodes called on CT-only chest exams.