Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089, USA.
Med Phys. 2013 Apr;40(4):041912. doi: 10.1118/1.4794480.
Visual analysis of three-dimensional (3D) coronary computed tomography angiography (CCTA) remains challenging due to large number of image slices and tortuous character of the vessels. The authors aimed to develop a robust, automated algorithm for unsupervised computer detection of coronary artery lesions.
The authors' knowledge-based algorithm consists of centerline extraction, vessel classification, vessel linearization, lumen segmentation with scan-specific lumen attenuation ranges, and lesion location detection. Presence and location of lesions are identified using a multi-pass algorithm which considers expected or "normal" vessel tapering and luminal stenosis from the segmented vessel. Expected luminal diameter is derived from the scan by automated piecewise least squares line fitting over proximal and mid segments (67%) of the coronary artery considering the locations of the small branches attached to the main coronary arteries.
The authors applied this algorithm to 42 CCTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥ 25%. The reference standard was provided by visual and quantitative identification of lesions with any stenosis ≥ 25% by three expert readers using consensus reading. The authors algorithm identified 42 lesions (93%) confirmed by the expert readers. There were 46 additional lesions detected; 23 out of 39 (59%) of these were less-stenosed lesions. When the artery was divided into 15 coronary segments according to standard cardiology reporting guidelines, per-segment basis, sensitivity was 93% and per-segment specificity was 81% using 10-fold cross-validation.
The authors' algorithm shows promising results in the detection of both obstructive and nonobstructive CCTA lesions.
由于冠状动脉计算机断层血管造影术（CCTA）的图像切片数量庞大且血管迂曲，因此其三维（3D）图像的可视化分析仍然具有挑战性。作者旨在开发一种稳健的、自动的冠状动脉病变计算机检测算法。
作者基于知识的算法包括中心线提取、血管分类、血管线性化、基于扫描的管腔衰减范围的管腔分割以及病变位置检测。使用多通道算法识别病变的存在和位置，该算法考虑了从分割后的血管中预期或“正常”的血管变细和管腔狭窄。预期的管腔直径通过在冠状动脉近端和中段（67%）上自动进行分段最小二乘拟合线，考虑到从主要冠状动脉附着的小分支的位置，从扫描中得出。
作者将该算法应用于 42 例双源 CT 采集的 CCTA 患者数据集，其中 21 例数据集有 45 处狭窄≥25%的病变。参考标准由三位专家读者通过共识阅读对任何狭窄≥25%的病变进行视觉和定量识别提供。作者的算法识别了 42 处病变（93%），这些病变得到了专家读者的确认。还检测到 46 处额外的病变；其中 39 处（59%）病变狭窄程度较低。当根据标准心脏病学报告指南将动脉分为 15 个冠状动脉节段时，使用 10 倍交叉验证，每节段的敏感性为 93%，特异性为 81%。
作者的算法在检测阻塞性和非阻塞性 CCTA 病变方面显示出良好的效果。
