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Abstract. The simulation of ultrasound wave propagation is of high interest in fields as ultrasound system development and therapeutic ultrasound. From a computational point of view the requirements for realistic simulations are immense with processing time reaching even an entire day. In this work we present a framework for fast ultrasound image simulation covering the imaging pipeline from the initial pulse transmission to the final image formation. The propagation of ultrasound waves is modeled with the Westervelt equation, which is solved explicitly with a Finite Difference scheme. Solving this scheme in parallel on the Graphics Processing Unit allows us to simulate realistic ultrasound images in a short time.

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Intravascular ultrasound (IVUS) is the predominant imaging modality in the field of interventional cardiology that provides real-time cross-sectional images of coronary arteries and the extent of atherosclerosis. Due to heterogeneity of lesions and stringent spatial/spectral behavior of tissues, atherosclerotic plaque characterization has always been a challenge and still is an open problem. In this paper, we present a systematic framework from in vitro data collection, histology preparation, IVUS-histology registration along with matching procedure, and finally a robust texture-derived unsupervised atherosclerotic plaque labeling. We have performed our algorithm on in vitro and in vivo images acquired with single-element 40 MHz and 64-elements phased array 20 MHz transducers, respectively. In former case, we have quantified results by local contrasting of constructed tissue colormaps with corresponding histology images employing an independent expert and in the latter case, virtual histology images have been utilized for comparison. We tackle one of the main challenges in the field that is the reliability of tissues behind arc of calcified plaques and validate the results through a novel random walks framework by incorporating underlying physics of ultrasound imaging. We conclude that proposed framework is a formidable approach for retrieving imperative information regarding tissues and building a reliable training dataset for supervised classification and its extension for in vivo applications.