Algorithm Optimization for Quantitative Analysis of Intravascular Optical Coherence Tomography Data

Optical Coherence Tomography (OCT) is rapidly becoming the method of choice for assessing arterial wall pathology in vivo. Atherosclerotic plaques can be diagnosed with high accuracy, including measurement of the thickness of fibrous caps, permitting an assessment of the risk of rupture. While the OCT image presents morphological information in highly resolved detail, it relies on interpretation of the images by trained readers for the identification of vessel wall components and tissue type. We developed a framework to aid the recognition of these atherosclerotic plaque constituents, based on the optical attenuation coefficient of the tissue. Based on a single-scattering model and using the point spread functions of individually characterized imaging catheters, several parameters need to be set in the analysis that may influence the results. Here, we present a simulation study used to optimize these parameters.