PATIENT SPECIFIC MODELS IN LUNG CANCER SCREENING WITH CT

描述(逐字摘自申请者摘要)：本报告的目的 研究是开发计算机辅助方法来促进筛查 螺旋CT在肺癌早期诊断中的应用。 现有筛查试验的支持者认为， 肺癌的外科治疗在于发现微结节肿瘤 (直径1-3毫米)。多层螺旋CT能够对 胸部具有高空间分辨率，并有可能可靠地检测到 肺小结节。然而，这些图像序列产生了非常大的 体积数据集，由300-600个轴向图像组成，对于 对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，以 从高分辨率Hct(HR-Hct)图像自动识别肺结节 从多层扫描仪获取的数据。该技术涉及一种基于模型的 一种分割方法，其中关于大小、形状、位置、 正常和病理结构的密度和其他属性将是 用于自动区分肺局灶性结节和正常结节 支气管血管解剖学。肺结节的一般先验模型和相关 将开发解剖学来指导基线CT图像的分割。 特定于患者的模型将从学习的解剖学信息中衍生出来 并用于分析后续的监视CT扫描。 实现这一目标的具体目标包括： [1]自动区分肺结节和正常肺 基线肺癌筛查HR-HCT检查中的支气管血管结构。 [2]检测间隔新的结节并重新定位以前检测到的结节 在基线监测后进行HR-Hct检查。 [3]测量自动结节检测和重新定位的准确性 在HR-Hct扫描上。 [4]为了比较放射科医生对HR-HCT扫描的准确性和解释时间， 在有无自动检测系统的协助下，针对 现有的结核检测方法。

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.