Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve

In this work we propose to validate the predictive capabilities of one-dimensional (1D) blood flow models with full three-dimensional (3D) models in the context of patient-specific coronary hemodynamics in hyperemic conditions. Such conditions mimic the state of coronary circulation during the acquisition of the Fractional Flow Reserve (FFR) index. Demonstrating that 1D models accurately reproduce FFR estimates obtained with 3D models has implications in the approach to computationally estimate FFR. To this end, a sample of 20 patients was employed from which 29 3D geometries of arterial trees were constructed, 9 obtained from coronary computed tomography angiography (CCTA) and 20 from intra-vascular ultrasound (IVUS). For each 3D arterial model, a 1D counterpart was generated. The same outflow and inlet pressure boundary conditions were applied to both (3D and 1D) models. In the 1D setting, pressure losses at stenoses and bifurcations were accounted for through specific lumped models. Comparisons between 1D models (FFR1D) and 3D models (FFR3D) were performed in terms of predicted FFR value. Compared to FFR3D, FFR1D resulted with a difference of 0.00 ± 0.03 and overall predictive capability AUC, Acc, Spe, Sen, PPV and NPV of 0.97, 0.98, 0.90, 0.99, 0.82, and 0.99, with an FFR threshold of 0.8. We conclude that inexpensive FFR1D simulations can be reliably used as a surrogate of demanding FFR3D computations.

[1]  P. H. van der Voort,et al.  Measurement of fractional flow reserve to assess the functional severity of coronary-artery stenoses. , 1996, The New England journal of medicine.

[2]  U. Siebert,et al.  Fractional flow reserve versus angiography for guiding percutaneous coronary intervention. , 2009, The New England journal of medicine.

[3]  D. F. Young,et al.  Effect of geometry on pressure losses across models of arterial stenoses. , 1976, Journal of biomechanics.

[4]  Pedro A. Lemos,et al.  Registration Methods for IVUS: Transversal and Longitudinal Transducer Motion Compensation , 2017, IEEE Transactions on Biomedical Engineering.

[5]  Stefan Baumann,et al.  Comparison of diagnostic value of a novel noninvasive coronary computed tomography angiography method versus standard coronary angiography for assessing fractional flow reserve. , 2014, The American journal of cardiology.

[6]  Boyang Su,et al.  Application of Patient-Specific Computational Fluid Dynamics in Coronary and Intra-Cardiac Flow Simulations: Challenges and Opportunities , 2018, Front. Physiol..

[7]  Dorin Comaniciu,et al.  Comparison of Fractional Flow Reserve Based on Computational Fluid Dynamics Modeling Using Coronary Angiographic Vessel Morphology Versus Invasively Measured Fractional Flow Reserve. , 2016, The American journal of cardiology.

[8]  Liang Zhong,et al.  Advanced analyses of computed tomography coronary angiography can help discriminate ischemic lesions. , 2018, International journal of cardiology.

[9]  A. Kono,et al.  Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm. , 2015, Radiology.

[10]  I. Meredith,et al.  Noninvasive CT-Derived FFR Based on Structural and Fluid Analysis: A Comparison With Invasive FFR for Detection of Functionally Significant Stenosis. , 2017, JACC. Cardiovascular imaging.

[11]  Yong-Jin Kim,et al.  Noninvasive diagnosis of ischemia-causing coronary stenosis using CT angiography: diagnostic value of transluminal attenuation gradient and fractional flow reserve computed from coronary CT angiography compared to invasively measured fractional flow reserve. , 2012, JACC. Cardiovascular imaging.

[12]  Liang Zhong,et al.  Numerical investigation of blood flow in three-dimensional porcine left anterior descending artery with various stenoses , 2014, Comput. Biol. Medicine.

[13]  Charles A. Taylor,et al.  Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. , 2013, Journal of the American College of Cardiology.

[14]  Charles A. Taylor,et al.  Uncertainty quantification in coronary blood flow simulations: Impact of geometry, boundary conditions and blood viscosity. , 2016, Journal of biomechanics.

[15]  J. Alastruey,et al.  A systematic comparison between 1‐D and 3‐D hemodynamics in compliant arterial models , 2014, International journal for numerical methods in biomedical engineering.

[16]  David A. Steinman,et al.  An image-based modeling framework for patient-specific computational hemodynamics , 2008, Medical & Biological Engineering & Computing.

[17]  Nan Xiao,et al.  On the impact of modelling assumptions in multi-scale, subject-specific models of aortic haemodynamics , 2016, Journal of The Royal Society Interface.

[18]  Federica Caforio,et al.  Assessment of reduced-order unscented Kalman filter for parameter identification in 1-dimensional blood flow models using experimental data. , 2017, International journal for numerical methods in biomedical engineering.

[19]  D. F. Young,et al.  Flow characteristics in models of arterial stenoses. II. Unsteady flow. , 1973, Journal of biomechanics.

[20]  Leo Grady,et al.  Impact of geometric uncertainty on hemodynamic simulations using machine learning , 2015 .

[21]  Peter Hunter Numerical simulation of arterial blood flow. , 1972 .

[22]  Ondřej Bublík,et al.  A comparative study of 1D and 3D hemodynamics in patient-specific hepatic portal vein networks , 2014 .

[23]  G. Mensah,et al.  Stroke volume/pulse pressure ratio and cardiovascular risk in arterial hypertension. , 1999, Hypertension.

[24]  L. Antiga,et al.  Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography , 2003, IEEE Transactions on Medical Imaging.

[25]  Patricia V. Lawford,et al.  Fast Virtual Fractional Flow Reserve Based Upon Steady-State Computational Fluid Dynamics Analysis , 2017, JACC. Basic to translational science.

[26]  Ghassan S. Kassab,et al.  A validated predictive model of coronary fractional flow reserve , 2012, Journal of The Royal Society Interface.

[27]  Gonzalo Maso Talou IVUS images segmentation driven by active contours and spacio-temporal reconstruction of the coronary vessels aided by angiographies , 2013 .

[28]  A. Dunning,et al.  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 Noninvasive Fractional Flow Reserve) study. , 2011, Journal of the American College of Cardiology.

[29]  J. P. Mynard,et al.  A unified method for estimating pressure losses at vascular junctions , 2015, International journal for numerical methods in biomedical engineering.

[30]  Liang Zhong,et al.  Combined diagnostic performance of coronary computed tomography angiography and computed tomography derived fractional flow reserve for the evaluation of myocardial ischemia: A meta-analysis. , 2017, International journal of cardiology.

[31]  Yangsoo Jang,et al.  Assessing Computational Fractional Flow Reserve From Optical Coherence Tomography in Patients With Intermediate Coronary Stenosis in the Left Anterior Descending Artery , 2016, Circulation. Cardiovascular interventions.

[32]  G. Karniadakis,et al.  Modeling Blood Flow Circulation in Intracranial Arterial Networks: A Comparative 3D/1D Simulation Study , 2010, Annals of Biomedical Engineering.

[33]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[34]  Liang Zhong,et al.  Simplified Models of Non-Invasive Fractional Flow Reserve Based on CT Images , 2016, PloS one.

[35]  Patricia V Lawford,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. , 2013, JACC. Cardiovascular interventions.

[36]  Ross T. Whitaker,et al.  Variable-conductance, level-set curvature for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[37]  Volker Klauss,et al.  Fractional flow reserve versus angiography for guidance of PCI in patients with multivessel coronary artery disease (FAME): 5-year follow-up of a randomised controlled trial , 2015, The Lancet.

[38]  Tim A. Fonte,et al.  Computational Fluid Dynamics Applied to Cardiac Computed Tomography for Noninvasive Quantification of Fractional Flow Reserve , 2022 .

[39]  Pablo J. Blanco,et al.  A high‐order local time stepping finite volume solver for one‐dimensional blood flow simulations: application to the ADAN model , 2016, International journal for numerical methods in biomedical engineering.

[40]  Michael J Pencina,et al.  Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. , 2012, JAMA.

[41]  Pedro A. Lemos,et al.  Improving Cardiac Phase Extraction in IVUS Studies by Integration of Gating Methods , 2015, IEEE Transactions on Biomedical Engineering.

[42]  P. Nithiarasu,et al.  Estimating the accuracy of a reduced‐order model for the calculation of fractional flow reserve (FFR) , 2018, International journal for numerical methods in biomedical engineering.

[43]  K. Gould,et al.  Pressure-derived fractional flow reserve to assess serial epicardial stenoses: theoretical basis and animal validation. , 2000, Circulation.

[44]  Y. Huo,et al.  A hybrid one-dimensional/Womersley model of pulsatile blood flow in the entire coronary arterial tree. , 2007, American journal of physiology. Heart and circulatory physiology.

[45]  Milan Sonka,et al.  3D catheter path reconstruction from biplane angiograms , 1998, Medical Imaging.

[46]  L. V. Vliet,et al.  Automatic segmentation, detection and quantification of coronary artery stenoses on CTA , 2013, The International Journal of Cardiovascular Imaging.

[47]  C A Bulant,et al.  A head-to-head comparison between CT- and IVUS-derived coronary blood flow models. , 2017, Journal of biomechanics.

[48]  William Wijns,et al.  Percutaneous coronary intervention of functionally nonsignificant stenosis: 5-year follow-up of the DEFER Study. , 2007, Journal of the American College of Cardiology.

[49]  Jean-Philippe Verhoye,et al.  Analog Electrical Model of the Coronary Circulation in Case of Multiple Revascularizations , 2008, Annals of Biomedical Engineering.