On-site evaluation of CT-based fractional flow reserve using simple boundary conditions for computational fluid dynamics
Fractional flow reserve (FFR) is an established method for diagnosing physiological coronary artery stenosis. A method for computing FFR using coronary computed tomography (CT) images was recently developed. However, its calculation requires off-site supercomputer analysis. Here, we report the preliminary result of a method using simple estimation of boundary conditions. The lumen boundaries of the coronary arteries were semi-automatically delineated using full width at half maximum of CT number profiles. The computational fluid dynamics (CFD) of the blood flow was performed using the boundary conditions of a fixed pressure at the coronary ostium and flow rates at each outlet. The total inflow at the coronary ostium was estimated based on the uniform wall shear stress hypothesis and corrected using a hyperemic multiplier to gain a hyperemic flow rate. The flow distribution from a parent vessel to the downstream daughter vessels was determined according to Murray’s law. FFR estimated by CFD was calculated as FFRCFD = Pd/Pa. We collected patients who underwent coronary CT and coronary angiography followed by invasively measured FFR and compared FFRCFD with FFR. Sensitivity, specificity, and correlations were assessed. A total of 48 patients and 72 arteries were assessed. The correlation coefficient of FFRCFD with FFR was 0.56. The cut-off value was ≤ 0.80, sensitivity was 59.1%, and specificity was 94.0%. CFD-based FFR using simple boundary conditions for on-site clinical computation provided FFRCFD values that were moderately correlated with invasively measured FFR.
KeywordsFractional flow reserve Computed tomography Coronary physiology Computational fluid dynamics
Compliance with ethical standards
Conflicts of interest
None of the authors have anything to declare regarding this article. This study was conducted as a part of the project focused on creation of medical arts by Japan Agency for Medical Research and Development (Grant Number JP17hk0102035).
Research involving human participants and/or animals
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
The need for written informed consent was waived because of the study’s retrospective design.
- 11.Koo BK, Erglis A, Doh JH et al (2011) Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms: results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noni. J Am Coll Cardiol 58:1989–1997. https://doi.org/10.1016/j.jacc.2011.06.066 CrossRefPubMedGoogle Scholar
- 12.Nakazato R, Park HB, Berman DS et al (2013) Noninvasive fractional flow reserve derived from computed tomography angiography for coronary lesions of intermediate stenosis severity results from the DeFACTO study. Circ Cardiovasc Imaging 6:881–889. https://doi.org/10.1161/CIRCIMAGING.113.000297 CrossRefPubMedGoogle Scholar
- 13.Nørgaard BL, Leipsic J, Gaur S et al (2014) Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: Next Steps). J Am Coll Cardiol 63:1145–1155. https://doi.org/10.1016/j.jacc.2013.11.043 CrossRefPubMedGoogle Scholar
- 14.Nozue T, Fukui K, Takamura T et al (2017) Effects of alogliptin on fractional flow reserve evaluated by coronary computed tomography angiography in patients with type 2 diabetes: rationale and design of the TRACT study. J Cardiol 69:518–522. https://doi.org/10.1016/j.jjcc.2016.04.014 CrossRefPubMedGoogle Scholar
- 19.Abbara S, Arbab-Zadeh A, Callister TQ et al (2009) SCCT guidelines for performance of coronary computed tomographic angiography: a report of the society of cardiovascular computed tomography guidelines committee. J Cardiovasc Comput Tomogr 3:190–204. https://doi.org/10.1016/j.jcct.2009.03.004 CrossRefPubMedGoogle Scholar
- 22.Lee JM, Choi G, Koo BK et al (2019) Identification of high-risk plaques destined to cause acute coronary syndrome using coronary computed tomographic angiography and computational fluid dynamics. JACC Cardiovasc Imaging 12:1032–1043. https://doi.org/10.1016/j.jcmg.2018.01.023 CrossRefPubMedGoogle Scholar
- 24.Shi C, Zhang D, Cao K et al (2017) A study of noninvasive fractional flow reserve derived from a simplified method based on coronary computed tomography angiography in suspected coronary artery disease. Biomed Eng Online 16:43. https://doi.org/10.1186/s12938-017-0330-2 CrossRefPubMedPubMedCentralGoogle Scholar
- 26.Tu S, Barbato E, Köszegi Z et al (2014) Fractional flow reserve calculation from 3-dimensional quantitative coronary angiography and TIMI frame count: a fast computer model to quantify the functional significance of moderately obstructed coronary arteries. JACC Cardiovasc Interv 7:768–777. https://doi.org/10.1016/j.jcin.2014.03.004 CrossRefPubMedGoogle Scholar