Abstract
Fractional flow reserve evaluation plays a crucial role in diagnosing ischemic coronary artery disease. Machine learning based fractional flow reserve evaluation has become the most important method due to it effectiveness and high computation efficiency. However, it still suffers from lacking of the proper description for the coronary artery fluid. This study presents a variational field constraint learning method for assessing fractional flow reserve from digital subtraction angiography images. Our method offers a promising approach by integrating governing equations and boundary conditions as unified constraints. By leveraging a holistic consideration of the fluid dynamics, our method achieves more accurate fractional flow reserve prediction compared to existing methods.
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Acknowledgments
This work was supported in part by The National Key Research and Development Program of China (2022YFE0209800), Shenzhen Science and Technology Program (GJHZ20220913142601003), National Natural Science Foundation of China (62101606, 62276282, 62101610, 62325113, 62271511), Guangdong Basic and Applied Basic Research Foundation (2024B1515020062, 2022A1515011384).
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Zhang, Q. et al. (2024). Variational Field Constraint Learning for Degree of Coronary Artery Ischemia Assessment. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15003. Springer, Cham. https://doi.org/10.1007/978-3-031-72384-1_72
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DOI: https://doi.org/10.1007/978-3-031-72384-1_72
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