Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/115963
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Title: Clinical applications of fractional flow reserve derived from computed tomography in coronary artery disease
Authors: Chan, C 
Wang, M
Kong, L 
Li, L
Chan, LWC 
Issue Date: Mar-2025
Source: Mayo clinic proceedings. Digital health, Mar. 2025, v. 3, no. 1, 100187
Abstract: Computer tomography–derived fractional flow reserve (CT-FFR) represents a significant advancement in noninvasive cardiac functional assessment. This technology uses computer simulation and anatomical information from computer tomography of coronary angiogram to calculate the CT-FFR value at each point within the coronary vasculature. These values serve as a critical reference for cardiologists in making informed treatment decisions and planning. Emerging evidence suggests that CT-FFR has the potential to enhance the specificity of computer tomography of coronary angiogram, thereby reducing the need for additional diagnostic examinations such as invasive coronary angiography and cardiac magnetic resonance imaging. This could result in savings in financial cost, time, and resources for both patients and health care providers. However, it is important to note that although CT-FFR holds great promise, there are limitations to this technology. Users should be cautious of common pitfalls associated with its use. A comprehensive understanding of these limitations is essential for effectively applying CT-FFR in clinical practice.
Publisher: Elsevier Inc.
Journal: Mayo clinic proceedings. Digital health 
EISSN: 2949-7612
DOI: 10.1016/j.mcpdig.2024.100187
Rights: © 2024 THE AUTHORS. Published by Elsevier Inc on behalf of Mayo Foundation for Medical Education and Research. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
The following publication Chan, C., Wang, M., Kong, L., Li, L., & Chi Chan, L. W. (2025). Clinical Applications of Fractional Flow Reserve Derived from Computed Tomography in Coronary Artery Disease. Mayo Clinic Proceedings: Digital Health, 3(1), 100187 is available at https://doi.org/10.1016/j.mcpdig.2024.100187.
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