@inproceedings{bbdce85d61eb4e8c8d6c829effc712cf,
title = "Variational Field Constraint Learning for Degree of Coronary Artery Ischemia Assessment",
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.",
keywords = "fractional flow reserve, hemodynamic, machine learning, variational form",
author = "Qi Zhang and Xiujian Liu and Heye Zhang and Chenchu Xu and Guang Yang and Yixuan Yuan and Tao Tan and Zhifan Gao",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 ; Conference date: 06-10-2024 Through 10-10-2024",
year = "2024",
doi = "10.1007/978-3-031-72384-1\_72",
language = "English",
isbn = "9783031723834",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "768--778",
editor = "Linguraru, \{Marius George\} and Aasa Feragen and Ben Glocker and Schnabel, \{Julia A.\} and Qi Dou and Stamatia Giannarou and Karim Lekadir",
booktitle = "Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 - 27th International Conference, Proceedings",
address = "Germany",
}