COMPUTER AIDED DIAGNOSIS IN CT OF THE THORAX

拟议研究的广泛和长期目标是 开发一个完全自动化的计算机化系统，帮助放射科医生 螺旋CT肺结节的检测及定量评价 胸部的计算机断层扫描（CT）图像。该系统将有可能 肺癌的早期诊断和治疗 诊断.螺旋CT是目前公认的最敏感的 肺结节的影像学评价模式。大量的图像 然而，在CT扫描期间获取的数据使得结节检测由人进行。 观察员是一项艰巨的任务。此外，区分结节和正常 例如肺血管的解剖结构通常需要在 多个CT切片，每个切片都包含必须评估的信息 并被吸收到体积数据的更大背景中 在扫描过程中获得。这种评估需要放射科医生在精神上 构建患者解剖结构的三维表示 在CT检查期间采集的50个切片图像。这项任务虽然繁琐， 对于放射科医师来说，可以通过计算机化方法有效地处理。的 拟议的研究项目将调查二维和 三维结构的肺结节在螺旋CT图像，以充分 利用在CT检查期间采集的体积图像数据。灰度 基于阈值的技术将用于提取三维结构 CT图像数据。计算的定量几何和灰度信息 将用作自动分类器的输入， 区分对应于结节的结构和 符合正常解剖结构。这种定量信息还将考虑到 基于放射学外观的检测性能评价 结节 本研究的具体目标是：（1）收集 正常和异常胸部螺旋CT扫描，（2）开发一个自动化的 在这些CT扫描中检测和定量评估肺结节的方法， (3)研究低剂量成像中结节外观的差异， 从肺癌筛查项目中获得的螺旋胸部CT扫描， 与标准螺旋CT相反，这些差异对 检测方案，以及（4）评估计算机化的性能 检测方案及其对放射科医生在任务中表现的影响 识别肺结节的能力

The broad, long term objective of the proposed research is to develop a fully automated, computerized system that will assist radiologists in the detection and quantitative assessment of pulmonary nodules in helical computed tomography (CT) images of the thorax. This system will potentially improve the prognosis of patients with lung cancer by contributing to earlier diagnosis. It is widely recognized that helical CT is the most sensitive imaging modality for the valuation of lung nodules. The large amount of image data acquired during a CT scan, however, makes nodule detection by human observers a difficult task. Moreover, distinguishing between nodules and normal anatomy such as pulmonary vessels typically requires visual comparison among multiple CT sections, each of which contains information that must be evaluated by a radiologist and assimilated into the larger context of the volumetric data acquired during the scan. This evaluation requires the radiologist to mentally construct a three-dimensional representation of patient anatomy based on over 50 section images acquired during a CT examination. This task, while cumbersome for radiologists, may be efficiently handled by a computerized method. The proposed research project will investigate the two-dimensional and three-dimensional structure of lung nodules in helical CT images to fully exploit the volumetric image data acquired during a CT examination. Gray-level threshold-based techniques will be used to extract three-dimensional structures from CT image data. Quantitative geometric and gray-level information computed for nodule candidates will be used as input to automated classifiers to distinguish between structures that correspond to nodules and structures that correspond to normal anatomy. This quantitative information will also allow for an evaluation of detection performance based on radiologic appearance of nodules. The specific aims of the proposed research are: (1) to collect databases of normal and abnormal helical thoracic CT scans, (2) to develop an automated method to detect and quantitatively assess pulmonary nodules in these CT scans, (3) to investigate differences in the appearance of nodules imaged in low-dose helical thoracic CT scans obtained from a lung cancer screening program as opposed to standard helical CT and the effect of these differences on the detection scheme, and (4) to evaluate the performance of the computerized detection scheme and its effect on the performance of radiologists in the task of identifying pulmonary nodules.