Objective To developed a data-driven method for fast calculation of coronary microcirculation resistance. Methods The neural network was constructed and optimized to extract cross-sectional area features of coronary arteries. The microcirculation resistance at the end of the coronary branch was quickly calculated by using cross-sectional area features, allometric scaling law and flow distribution ratio, and the blood flow reserve fraction was non-invasively calculated based on microcirculation resistance. Results In order to verify validity of the neural network, the cross-sectional area characteristics of 40 clinically collected coronary artery branch measurements were compared with predicted result of the neural network, and the mean absolute error value was 1. 08 mm2 . In order to verify accuracy of the microcirculation resistance, the clinical fractional flow reserve of 15 patients was compared with the fractional flow reserve calculated by the microcirculation resistance, and the calculation accuracy was 86. 6% . Conclusions The rapid calculation method of coronary microcirculation resistance proposed in this study has potential clinical application value.
SUN Hao, LI Bao, LIU Jincheng, LI Na, LIU Jian, LIU Youjun. Data-Driven Rapid Calculation Method of Coronary Microcirculation Resistance[J]. Journal of medical biomechanics,2022,37(6):1119-1126Copy