Abstract

Atherosclerotic plaque has been identified as one of the most important causes of sudden cardiac failure in patients with no history of heart disease. IntraVascular UltraSound (IVUS) represents a unique technique to study, determine and quantify plaque composition and thus allows to develop automatic diagnostic and prediction techniques for coronary diagnosis and therapy. However, one of the main problems of image-based studies is its dependence on image brightness and data miss-registration due to the dynamic system composed by the catheter and the vessel. Hence, the high dependence of the automatic analysis on the gain setting of IVUS console and its transmit power as well as vessel motion make impossible direct analysis, comparison and follow up of IVUS studies. To this purpose, a complete framework for data analysis should be considered focusing on: a) modeling the image acquisition and formation process, b) developing techniques for removing data acquisition artifacts due to the nature of ultrasound reflectance and motion of coronary vessels, c) developing sophisticated tools for extracting features from radio-frequency and images, and d) designing robust methods to discover and classify different categories of tissue structures. In this chapter, we overview different methodologies to approach the afore-mentioned problems and outline possible computer-assisted applications in the clinical practice.