A Gamma Mixture Model for IVUS Imaging

Abstract

Detection and characterization of vulnerable plaques is of paramount importance for early disease diagnosis and treatment of carotid and coronary vascular accidents, which are mostly caused by this type of plaques. In this scope, the echo-morphology and composition are described in the literature, in statistical frameworks, by using several distributions to describe ultrasonic data depending on tissues, acquisition conditions, and equipment. Among them, the Rayleigh distribution and one of its generalization, the Nakagami distribution, have been extensively used to describe the raw envelope RF ultrasound signal. However, they fail to describe the statistical properties throughout some stages of the acquisition processes. Specifically, some signal-processing procedures usually used in the common ultrasound scanners involve linear filters or interpolations which affect the statistics of the signal and, eventually, change the assumed distribution of the signal. In this chapter, we provide an analysis of the statistical changes of the signal throughout the acquisition process of intravascular ultrasonography. The way interpolation and linear filtering procedures affect the statistics of the RF raw data receives here particular attention. In fact, we show that Gamma distribution is more suitable for statistical description of RF signal than the usual Rayleigh or Nakagami distributions, mainly because the interpolation and filtering procedures present in the US raw data processing pipeline. In this perspective, a Gamma mixture model (GMM) is proposed to describe the RF envelope images as natural evolution of a similar approach using Rayleigh distributions proposed before. This model is compared to the Rayleigh and Nagakami mixture models and the comparison results evidence a better performance with respect to plaque description and characterization of echogenic contents. As an application of this characterization, a probabilistic filter with detail preservation is proposed for detection and classification purposes of different type of tissues within the plaque. This tissue constitution information is the basis for plaque risk assessment and vascular accident prevention.