Quantification of intramural calcification in coronary intravascular ultrasound images with automated image analysis

Background: Recent studies have documented the utility of intravascular ultrasonography in quantifying coronary morphologic characteristics and determining on 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. Methods: 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. Results: 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. Conclusion: The accuracy of this automated technique was comparable to interoperator and intraoperator variability in manually tracing calcification.