Non-invasive Computational Techniques for Diagnosing Myocardial Ischemia: Challenges and Future of FFR<sub>CT</sub>/iFR<sub>CT</sub>.
Authors
Affiliations (7)
Affiliations (7)
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing, China.
- Center for Medical Metrology, National institute of metrology, 18 North Third Ring East Road, Chaoyang District, Beijing, China. [email protected].
- School of Mechanics and Engineering Science, State Key Laboratory for Turbulence and Complex Systems, Peking University, Beijing, China.
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, Singapore.
- Department of Biological Engineering, School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
- Department of Cardiology, Peking University People's Hospital, Beijing, China.
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing, China. [email protected].
Abstract
Computational fluid dynamics (CFD)-based numerical calculation of fractional flow reserve (FFR<sub>CT</sub>) and instantaneous wave-free ratio (iFR<sub>CT</sub>) is a crucial non-invasive technology for assessing myocardial ischemia. Their diagnostic accuracy depends on precisely calculating epicardial coronary stenosis resistance and coronary microcirculatory resistance. However, conventional CFD models face two main limitations. First, they assume rigid vessel walls, failing to capture how different plaque types affect stenosis resistance through neural regulation-induced vasodilation changes. Second, the presence of compensatory mechanisms in coronary microcirculation leads to inaccuracies in calculating microcirculatory resistance. These physiological oversimplifications, combined with the low computational efficiency of traditional CFD, compromise diagnostic accuracy and hinder real-time clinical application. This paper systematically reviews existing FFR<sub>CT</sub>/iFR<sub>CT</sub> computational models, their limitations, and current research on model improvement and computational efficiency. Given the acute nature and high mortality of myocardial infarction, future efforts should focus on establishing high-fidelity cardiovascular simulation models by integrating multi-modal clinical data. Combining artificial intelligence with digital twin technology could enable dynamic early warning for acute myocardial ischemia and infarction in daily life applications. This direction represents a promising future development path for non-invasive diagnostic technologies and holds significant clinical value.