Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX, USA.
Int J Cardiovasc Imaging. 2010 Oct;26(7):829-38. doi: 10.1007/s10554-010-9608-1. Epub 2010 Mar 16.
Measurements related to coronary artery calcification (CAC) offer significant predictive value for coronary artery disease (CAD). In current medical practice CAC scoring is a labor-intensive task. The objective of this paper is the development and evaluation of a family of coronary artery region (CAR) models applied to the detection of CACs in coronary artery zones and sections. Thirty patients underwent non-contrast electron-beam computed tomography scanning. Coronary artery trajectory points as presented in the University of Houston heart-centered coordinate system were utilized to construct the CAR models which automatically detect coronary artery zones and sections. On a per-patient and per-zone basis the proposed CAR models detected CACs with a sensitivity, specificity and accuracy of 85.56 (± 15.80)%, 93.54 (± 1.98)%, and 85.27 (± 14.67)%, respectively while the corresponding values in the zones and segments based case were 77.94 (± 7.78)%, 96.57 (± 4.90)%, and 73.58 (± 8.96)%, respectively. The results of this study suggest that the family of CAR models provide an effective method to detect different regions of the coronaries. Further, the CAR classifiers are able to detect CACs with a mean sensitivity and specificity of 86.33 and 93.78%, respectively.
冠状动脉钙化（CAC）相关测量对冠状动脉疾病（CAD）具有重要的预测价值。在当前的医学实践中，CAC 评分是一项劳动密集型任务。本文的目的是开发和评估一组应用于冠状动脉区域（CAR）模型，以检测冠状动脉区域和节段中的 CAC。 30 名患者接受了非对比电子束计算机断层扫描。利用休斯顿大学心脏中心坐标系中的冠状动脉轨迹点构建了 CAR 模型，该模型可自动检测冠状动脉区域和节段。基于每位患者和每个区域的基础上，提出的 CAR 模型对 CAC 的检测灵敏度、特异性和准确率分别为 85.56（±15.80）%、93.54（±1.98）%和 85.27（±14.67）%，而基于病例的区域和节段的相应值分别为 77.94（±7.78）%、96.57（±4.90）%和 73.58（±8.96）%。 这项研究的结果表明，CAR 模型组提供了一种有效检测冠状动脉不同区域的方法。此外，CAR 分类器能够以平均灵敏度和特异性 86.33%和 93.78%分别检测 CAC。
