Lee Kyung Eun, Ryu Ah-Jin, Shin Eun-Seok, Shim Eun Bo
Department of Mechanical and Biomedical Engineering, Kangwon National University, Kangwondaehak-gil, Chuncheon-si, Kangwon-do, 200-701, Republic of Korea.
Department of Cardiology, University of Ulsan College of Medicine, Ulsan, South Korea.
Pflugers Arch. 2017 Jun;469(5-6):613-628. doi: 10.1007/s00424-017-1961-7. Epub 2017 Mar 28.
This work reviews the key aspects of coronary and neurovascular flow reserves with an emphasis on physiomic modeling characteristics by the use of a variety of numerical approaches. First, we explain the definition of fractional flow reserve (FFR) in coronary artery and introduce its clinical significance. Then, computational researches for obtaining FFR are reviewed, and their clinical outcomes are compared. In the case of cerebrovascular reserve (CVR), in spite of substantial progress in the simulation of cerebral hemodynamics, only a few computational studies exist. Thus, we discuss the limitations of CVR simulation study and suggest the challenging issue to overcome these. Also, the future direction of physiomic researches for the flow reserves in coronary arteries and cerebral arteries is described. Also, we introduce a machine learning algorithm trained by the existing physiomic simulation data of flow reserve and suggest a prospective research direction related to this.
这项工作回顾了冠状动脉和神经血管血流储备的关键方面,重点是通过使用各种数值方法的生理组学建模特征。首先,我们解释冠状动脉中血流储备分数(FFR)的定义,并介绍其临床意义。然后,回顾了获取FFR的计算研究,并比较了它们的临床结果。对于脑血管储备(CVR),尽管在脑血流动力学模拟方面取得了重大进展,但只有少数计算研究。因此,我们讨论了CVR模拟研究的局限性,并提出了克服这些局限性的具有挑战性的问题。此外,还描述了冠状动脉和脑动脉血流储备的生理组学研究的未来方向。此外,我们介绍了一种通过现有的血流储备生理组学模拟数据训练的机器学习算法,并提出了与此相关的前瞻性研究方向。