COMPUTER AIDED DIAGNOSIS IN CT OF THE THORAX

拟议研究的广泛、长期目标是 开发一种全自动化、计算机化的系统，帮助放射科医生 螺旋CT对肺结节的检出及定量评价 胸部的计算机断层扫描(CT)图像。这个系统将有可能 通过早期治疗改善肺癌患者的预后 诊断。人们普遍认为螺旋CT是最敏感的 评价肺结节的影像方法。海量的图像 然而，在CT扫描期间获得的数据使人类能够检测到结节 观察是一项艰巨的任务.此外，区分结节和正常 解剖结构，如肺血管，通常需要在 多个CT部分，每个部分都包含必须评估的信息 由放射科医生提供，并被吸收到体积数据的更大范围内 在扫描过程中获取的。这项评估要求放射科医生在精神上 基于Over构建患者解剖的三维表示 CT检查过程中采集的50张断层图像。这项任务虽然繁琐，但 对于放射科医生来说，可以通过计算机化的方法有效地处理。这个 拟议的研究项目将调查二维和 充分显示螺旋CT图像中肺结节的三维结构 利用在CT检查期间获得的体积图像数据。灰度 基于阈值的技术将被用于提取三维结构 从CT图像数据。计算的定量几何和灰度级信息 对于候选结核，将用作自动分类器的输入以 区分与结核相对应的结构和与之对应的结构 与正常解剖结构相对应。这一定量信息还将允许 基于放射学表现的检测性能评价 结节。 拟议研究的具体目标是：(1)收集 正常和异常的胸部螺旋CT扫描，(2)开发一种自动化 在这些CT扫描中检测和定量评估肺结节的方法， (3)探讨低剂量组中结节表现的差异。 从肺癌筛查计划获得的螺旋胸部CT扫描 与标准螺旋CT的对比以及这些差异对 检测方案，以及(4)评估计算机化的性能 检查方案及其对放射科医生工作绩效的影响 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.