PATIENT SPECIFIC MODELS IN LUNG CANCER SCREENING WITH CT

CT 肺癌筛查中的患者特异性模型

• 批准号: 6498036
• 负责人: MATTHEW S BROWN
• 金额: $ 27.25万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-02-16 至 2005-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6498036
• 关键词: biomedical automation; clinical research; computed axial tomography; computer assisted diagnosis; computer system design /evaluation; diagnosis design /evaluation; human subject; image processing; lung neoplasms; neoplasm /cancer diagnosis

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.

描述（逐字病申请人的摘要）：此目的 研究是开发计算机辅助的方法，以促进筛查 使用螺旋计算机断层扫描（HCT）对肺癌的早期检测。 现有筛查试验的支持者认为， 肺癌的手术治疗在于检测微型肿瘤 （直径为1-3毫米）。多片HCT能够成像整个 胸部以高空间分辨率，有可能可靠地检测到 肺微管。但是，这些图像序列产生的很大 由300-600个轴向图像组成的音量数据集，与 在当前的放射学实践中进行审查。 该建议涉及开发和实验测试 自动从高分辨率HCT（HR-HCT）图像中识别肺结节 从多板扫描仪获取的数据。该技术涉及基于模型的 细分方法中有关大小，形状，位置的信息， 正常和病理结构的密度和其他特性将是 用于自动化局灶性肺结节的自动化 支气管血管解剖结构。肺结节和相关的通用，先验模型 解剖学将开发以指导基线CT图像的分割。 特定于患者的模型将源自学到的解剖学信息 从基线扫描到用于分析随后的监视CT扫描。 实现此目的的具体目的是： [1]自动区分肺结节和正常肺 基线肺癌筛查HR-HCT检查中的支气管结构。 [2]检测间隔新结节并重新定位先前检测到的结节 在基线后监视HR-HCT考试中。 [3]测量自动结节检测和重新定位的准确性 在HR-HCT扫描中。 [4]比较HR-HCT扫描的放射科医生的准确性和解释时间， 在自动检测系统的协助下和没有任何帮助的情况下 预先存在的结节检测方法。