Coronary Artery Disease is one of the leading causes of deaths worldwide, with an estimated 7.2 million deaths each year. In spite of the improvements in imaging and other diagnostic modalities, the incidence of premature morbidity and mortality is still very high, the main reason being the lack of accurate in-vivo and in-vitro patient-specific estimates for diagnosis and disease progression. Recently, CFD-based models have been proposed for analyzing the coronary circulation [1, 2]. The main challenges for such methods are the lack of patient-specific data (anatomy, boundary conditions), inefficient multi-scale coupling and computational resources. These challenges limit the scope of such methods in a routine clinical setting.

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