PATIENT SPECIFIC MODELS IN LUNG CANCER SCREENING WITH CT

描述（来自申请人摘要的逐字描述）： 研究是开发计算机辅助方法，以方便筛选 使用螺旋计算机断层扫描（hCT）早期检测肺癌。 现有筛查试验的支持者认为， 肺癌的外科治疗在于发现微小结节肿瘤 (of直径1 - 3 mm）。多层hCT能够成像整个 胸部在高空间分辨率，并有可能可靠地检测 肺微结节然而，这些图像序列生成非常大的 体积数据集，由300 - 600个轴向图像组成， 当前放射学实践的回顾。 该提案涉及一种方法的开发和实验测试， 从高分辨率hCT（HR-hCT）图像自动识别肺结节 从多切片扫描仪获得的数据。该技术涉及基于模型的 分割方法，其中关于尺寸，形状，位置， 正常和病理结构的密度和其他性质将被 用于自动区分局灶性肺结节和正常肺结节 支气管血管解剖学肺结节的一般先验模型和相关的 将开发解剖结构以指导基线CT图像的分割。 患者特定模型将从所学习的解剖信息中导出 并用于分析随后的监测CT扫描。 实现这一目标的具体目标是： [1]自动区分肺结节和正常肺结节 支气管血管结构对基线肺癌筛查HR-hCT检查的影响。 [2]检测间隔新结节并重新定位先前检测到的结节 基线后监测HR-hCT检查。 [3]测量自动结节检测和重新定位的准确性 HR-hCT扫描 [4]为了比较放射科医师的准确性和HR-hCT扫描的判读时间， 无论是否有自动检测系统的帮助， 现有的结节检测方法。

DESCRIPTION (Verbatim from Applicant's Abstract): The objective of this research is to develop computer-assisted methods to facilitate screening for the early detection of lung cancer using helical computed tomography (hCT). Proponents of existing screening trials argue that the highest enhance of surgical cure from lung cancer lies in the detection of micronodular neoplasms (of 1-3 mm in diameter). Multi-slice hCT is capable of imaging the entire thorax at high spatial resolution and has the potential to reliably detect pulmonary micronodules. However, these image sequences generate extremely large volume data sets, consisting of 300-600 axial images, that are impractical to review in current radiology practice. This proposal involves development and experimental testing of a method to automatically identify lung nodules from high resolution hCT (HR-hCT) image data acquired from multi-slice scanners. The technique involves a model-based segmentation approach in which information about the size, shape, location, density and other properties of both normal and pathological structures will be used to automate the discrimination of focal lung nodules from normal bronchovascular anatomy. A generic, a priori model of lung nodules and relevant anatomy will be developed to guide segmentation of baseline CT images. Patient-specific models will be derived from the anatomical information learned from baseline scans and used to analyze subsequent surveillance CT scans. The specific aims to accomplish this are: [1] To automatically distinguish lung nodules from normal pulmonary bronchovascular structures on baseline lung cancer screening HR-hCT exams. [2] To detect interval new nodules and re-localize previously detected nodules on post-baseline surveillance HR-hCT exams. [3] To measure the accuracy of automated nodule detection and re-localization on HR-hCT scans. [4] To compare radiologist accuracy and interpretation times of HR-hCT scans, both with and without assistance from the automated detection system, against pre-existing nodule detection methods.