AUTOMATIC 3D REGISTRATION FOR ENHANCED CANCER MANAGEMENT

描述(由申请人提供) 在技术发展的推动下，肿瘤学家/放射科医生目前 在不断增加的更高分辨率的解剖和 功能成像数据。通用电气新推出的光速CT扫描仪只是其中之一 这类新的采集设备的典型例子。当务之急是新的 开发图像处理工具以帮助专家过滤 海量数据以保持甚至提高其敏感度 在检测肿瘤性改变方面具有较高的特异性。我们相信这笔赠款 是朝着这个重要方向迈出的重要一步，代表着最好的 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注册是一项建议 支持对高级图像能力进行有限的临床前测试 通过常规剔除来改善癌症管理的加工技术 临床医生可从现有诊断中获得更多更早的信息 医疗模式。该计划项目赠款包括a)在PRE中使用FMRI (神经外科)专注于运动障碍患者的手术计划 与运动带附近的病变有关，他们永远不会留下来 对于当前的fMRI技术来说，足够静止，b)支持病变 重复、间歇CT和超声的变化检测和量化 检查，以监测并可能根据以下情况修改治疗计划 个人患者反应，c)在确定放射中的器官方面的自动化 使用非增强CT数据集的治疗计划，以及d) 一种CT和PET配准减少假性的方法研究 仅在胸部CT检查时出现阳性结节。

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.