Masseroli M, Cothren R M, Meier D S, Tuzcu E M, Vince D G, Nissen S E, Thomas J D, Cornhill J F
Department of Biomedical Engineering, The Cleveland Clinic Foundation, Ohio 44195, USA.
Am Heart J. 1998 Jul;136(1):78-86. doi: 10.1016/s0002-8703(98)70185-1.
Recent studies have documented the utility of intravascular ultrasonography in quantifying coronary morphologic characteristics and determining an appropriate intervention. Unfortunately, its potential for quantifying lesion calcification is limited by subjective evaluation and manual tracing. The aim of this study was to develop an objective automated method for quantifying calcification in intracoronary images with digital image analysis.
Images of human coronary arteries acquired with a 30 MHz intracoronary ultrasound catheter were evaluated with digital image analysis and compared with manual tracings. Calcifications were automatically identified as highly echogenic regions detected by global thresholding within sectors of acoustic shadowing defined as regions devoid of texture.
The mean percentage agreement, sensitivity, and specificity of detecting calcification in 1-degree sectors of calcified vessels were 82%, 73%, and 87%, respectively. Similar results were obtained in noncalcified images.
The accuracy of this automated technique was comparable to interoperator and intraoperator variability in manually tracing calcification.
最近的研究已证明血管内超声在量化冠状动脉形态特征及确定合适干预措施方面的效用。不幸的是，其量化病变钙化的潜力受到主观评估和手动追踪的限制。本研究的目的是开发一种利用数字图像分析对冠状动脉内图像钙化进行量化的客观自动化方法。
使用30MHz冠状动脉内超声导管获取的人类冠状动脉图像通过数字图像分析进行评估，并与手动追踪结果进行比较。钙化被自动识别为在定义为无纹理区域的声学阴影扇区内通过全局阈值检测到的高回声区域。
在钙化血管的1度扇区中检测钙化的平均一致性百分比、敏感性和特异性分别为82%、73%和87%。在非钙化图像中也获得了类似结果。
这种自动化技术的准确性与手动追踪钙化时操作者间和操作者内的变异性相当。
