Coronary plaque classification through intravascular ultrasound radiofrequency data analysis using self-organizing map

Abstract

Intravascular ultrasound (IVUS) is an important clinical tool in the assessment of atherosclerotic plaque in coronary artery diseases. Using IVUS, we can obtain high resolution echo image of cross-sections of the coronary artery. However, it is difficult to accurately classify plaques by using the echogram only. We propose a method of IVUS Radiofrequency (RF) signal classification using self-organizing map (SOM). Characteristic ROIs (region of interest) of the IVUS echogram of patients with coronary lesions were selected by an expert medical doctor, and the SOM learned from these ROIs. The SOM could classify the RF signals with accuracies of 95.9% for fibrous plaque, 99.5% for blood, 96.2% for calcified plaque and 16.3% for media regions. This result suggests that the proposed technique is useful for automatic characterization of plaque in coronary artery.