The intravascular ultrasound-based tissue characterization of coronary plaque is important for the early diagnosis of acute coronary syndromes. The conventional tissue characterization techniques however cannot obtain sufficient identification accuracy for various tissue properties, because the feature employed for characterization are static features, which lack dynamical information about backscattered radio-frequency (RF) signals. In this work, we propose a new intravascular ultrasound-based tissue characterization method that uses a modular network self-organizing map (mnSOM) in which each module is composed of an autoregressive model for representing the dynamics of the RF signals. The proposed method can create a map of various dynamical features from the RF signal. This map enables generalized tissue characterizations. The proposed method is verified by comparing its tissue characterization performance with that of the conventional method using real intravascular ultrasound signals. 
Intravascular ultrasound-based tissue characterization using modular network self-organizing map