Non-invasive Quantification of Coronary Artery Disease in Arterial Bifurcations Using CCTA and CFD: Comparison to Fractional Flow Reserve Measurements
- 2.1k Downloads
Recent advances in coronary computed tomography angiography (CCTA) allow the calculation of various functional indices of coronary artery disease (CAD). smartFFR is our proposed new index for the assessment of the significance of coronary stenoses in coronary bifurcations. The aim of the current study is to compare smartFFR with the Fractional Flow Reserve (FFR) values deriving from direct invasive pressure measurements from a dedicated pressure wire. In the context of the SMARTool study, 22 patients with chest pain symptoms and intermediate pre-test likelihood of CAD underwent CCTA as well as FFR measurement. The 22 left arterial branches which included the LAD and the LCx were reconstructed using our in-house developed software. We performed two computational blood flow simulations for each case to calculate the smartFFR for each 3D model. Regarding the inlet, the average patient-specific pressure at rest was applied as a boundary condition. Assuming a myocardial blood flow of 2 ml/s and 6 ml/s during rest and under stress for the Left Main artery, respectively, we calculated the flow for each branch using Murray’s law and applied it as outlet boundary conditions. smartFFR was calculated for each branch by computing the ratio of distal to proximal pressure for a range of flows between 0 and 4 ml/s, normalized by the respective ratio of a normal artery. The required average process time was less than 20 min. Strong correlation (r = 0.88, P < 0.0001) was found between the two methods. All pathological cases presenting ischemia, were correctly categorized by our method as hemodynamically significant lesions. smartFFR demonstrated a high diagnostic accuracy for distinguishing hemodynamically significant lesions in a matter of minutes, and may represent a valid non-invasive tool for comprehensive characterization of CAD.
KeywordssmartFFR FFR CCTA
This work is part-funded by the European Commission. SMARTool simulation modelling in coronary artery disease: a tool for clinical decision support. GA 689068.
Conflict of Interest
The authors declare that there are no conflicts of interest.
- 1.M. Renker, U. J. Schoepf, R. Wang, F. G. Meinel, J. D. Rier, R. R. Bayer, et al., “Comparison of Diagnostic Value of a Novel Noninvasive Coronary Computed Tomography Angiography Method Versus Standard Coronary Angiography for Assessing Fractional Flow Reserve,” American Journal of Cardiology, vol. 114, pp. 1303–1308, Nov 1 2014.Google Scholar
- 2.M. Kruk, L. Wardziak, M. Demkow, W. Pleban, J. Pregowski, Z. Dzielinska, et al., “Workstation-Based Calculation of CTA-Based FFR for Intermediate Stenosis,” Jacc-Cardiovascular Imaging, vol. 9, pp. 690–699, Jun 2016.Google Scholar
- 3.J. K. Min, J. Leipsic, M. J. Pencina, D. S. Berman, B. K. Koo, C. van Mieghem, et al., “Diagnostic Accuracy of Fractional Flow Reserve From Anatomic CT Angiography,” Jama-Journal of the American Medical Association, vol. 308, pp. 1237–1245, Sep 26 2012.Google Scholar
- 4.P. D. Morris, D. Ryan, A. C. Morton, R. Lycett, P. V. Lawford, D. R. Hose, et al., “Virtual fractional flow reserve from coronary angiography: modeling the significance of coronary lesions: results from the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study,” JACC Cardiovasc Interv, vol. 6, pp. 149–57, Feb 2013.Google Scholar
- 5.M. I. Papafaklis, T. Muramatsu, Y. Ishibashi, L. S. Lakkas, S. Nakatani, C. V. Bourantas, et al., “Fast virtual functional assessment of intermediate coronary lesions using routine angiographic data and blood flow simulation in humans: comparison with pressure wire - fractional flow reserve,” EuroIntervention, vol. 10, pp. 574–83, Sep 2014.Google Scholar
- 6.L. Athanasiou, G. Rigas, A. I. Sakellarios, T. P. Exarchos, P. K. Siogkas, C. V. Bourantas, et al., “Three-dimensional reconstruction of coronary arteries and plaque morphology using CT angiography–comparison and registration with IVUS,” BMC Med Imaging, vol. 16, p. 9, 2016.Google Scholar