A head-to-head comparison between CT- and IVUS-derived coronary blood flow models.

The goal of this work is to compare coronary hemodynamics as predicted by computational blood flow models derived from two imaging modalities: coronary computed tomography angiography (CCTA) and intravascular ultrasound integrated with angiography (IVUS). Criteria to define boundary conditions are proposed to overcome the dissimilar anatomical definition delivered by both modalities. The strategy to define boundary conditions is novel in the present context, and naturally accounts for the flow redistribution induced by the resistance of coronary vessels. Hyperemic conditions are assumed to assess model predictions under stressed hemodynamic environments similar to those encountered in Fractional Flow Reserve (FFR) calculations. As results, it was found that CCTA models predict larger pressure drops, higher average blood velocity and smaller FFR. Concerning the flow rate at distal locations in the major vessels of interest, it was found that CCTA predicted smaller flow than IVUS, which is a consequence of a larger sensitivity of CCTA models to coronary steal phenomena. Comparisons to in-vivo measurements of FFR are shown.

[1]  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.

[2]  M. Schaap,et al.  3D reconstruction techniques of human coronary bifurcations for shear stress computations. , 2014, Journal of biomechanics.

[3]  Marcelo J. Vénere,et al.  HeMoLab - Hemodynamics Modelling Laboratory: An application for modelling the human cardiovascular system , 2012, Comput. Biol. Medicine.

[4]  Habib Samady,et al.  An assessment of intra-patient variability on observed relationships between wall shear stress and plaque progression in coronary arteries , 2015, Biomedical engineering online.

[5]  Michail I. Papafaklis,et al.  Relation of distribution of coronary blood flow volume to coronary artery dominance. , 2013, American Journal of Cardiology.

[6]  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.

[7]  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.

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

[9]  P. Yock,et al.  Practice and potential pitfalls of coronary pressure measurement , 2000, Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions.

[10]  Caixia Chen,et al.  Computational fluid dynamics tools can be used to predict the progression of coronary artery disease , 2006 .

[11]  P J Blanco,et al.  A computational approach to generate concurrent arterial networks in vascular territories , 2013, International journal for numerical methods in biomedical engineering.

[12]  A. Wahle,et al.  Effect of Endothelial Shear Stress on the Progression of Coronary Artery Disease, Vascular Remodeling, and In-Stent Restenosis in Humans: In Vivo 6-Month Follow-Up Study , 2003, Circulation.

[13]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[14]  Habib Samady,et al.  Computational Fluid Dynamics Simulations of Hemodynamics in Plaque Erosion , 2013, Cardiovascular engineering and technology.

[15]  Free and open-source software application for the evaluation of coronary computed tomography angiography images. , 2012, Arquivos brasileiros de cardiologia.

[16]  A. Knez,et al.  Quantification of Obstructive and Nonobstructive Coronary Lesions of 64-Slice Computed Tomography , 2005 .

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

[18]  T. Hughes,et al.  A new finite element formulation for computational fluid dynamics: VI. Convergence analysis of the generalized SUPG formulation for linear time-dependent multi-dimensional advective-diffusive systems , 1987 .

[19]  Thomas J. R. Hughes,et al.  A new finite element formulation for computational fluid dynamics: IX. Fourier analysis of space-time Galerkin/least-squares algorithms , 1991 .

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

[21]  Michail I. Papafaklis,et al.  Natural History of Experimental Coronary Atherosclerosis and Vascular Remodeling in Relation to Endothelial Shear Stress: A Serial, In Vivo Intravascular Ultrasound Study , 2010, Circulation.

[22]  James H. Brown,et al.  A General Model for the Origin of Allometric Scaling Laws in Biology , 1997, Science.

[23]  Francesco Burzotta,et al.  Maximal hyperemia in the assessment of fractional flow reserve: intracoronary adenosine versus intracoronary sodium nitroprusside versus intravenous adenosine: the NASCI (Nitroprussiato versus Adenosina nelle Stenosi Coronariche Intermedie) study. , 2012, JACC. Cardiovascular interventions.

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

[25]  Witold Rużyłło,et al.  Accuracy of coronary computed tomography angiography vs intravascular ultrasound for evaluation of vessel area. , 2014, Journal of cardiovascular computed tomography.

[26]  E LorensenWilliam,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987 .

[27]  E. Edelman,et al.  Prediction of the Localization of High-Risk Coronary Atherosclerotic Plaques on the Basis of Low Endothelial Shear Stress: An Intravascular Ultrasound and Histopathology Natural History Study , 2008, Circulation.

[28]  C A Bulant,et al.  A computational framework to characterize and compare the geometry of coronary networks , 2017, International journal for numerical methods in biomedical engineering.

[29]  A. Hughes,et al.  Differences in cardiac microcirculatory wave patterns between the proximal left mainstem and proximal right coronary artery. , 2008, American journal of physiology. Heart and circulatory physiology.

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

[31]  Konstantin Nikolaou,et al.  Quantification of obstructive and nonobstructive coronary lesions by 64-slice computed tomography: a comparative study with quantitative coronary angiography and intravascular ultrasound. , 2005, Journal of the American College of Cardiology.

[32]  T. Villines,et al.  Coronary CT angiography versus intravascular ultrasound for estimation of coronary stenosis and atherosclerotic plaque burden: a meta-analysis. , 2013, Journal of cardiovascular computed tomography.

[33]  K. Gould,et al.  Is discordance of coronary flow reserve and fractional flow reserve due to methodology or clinically relevant coronary pathophysiology? , 2012, JACC. Cardiovascular imaging.

[34]  J. Reiber,et al.  The impact of image resolution on computation of fractional flow reserve: coronary computed tomography angiography versus 3-dimensional quantitative coronary angiography , 2016, The International Journal of Cardiovascular Imaging.

[35]  F. N. van de Vosse,et al.  The influence of boundary conditions on wall shear stress distribution in patients specific coronary trees. , 2011, Journal of biomechanics.

[36]  Vartan Kurtcuoglu,et al.  Choosing the optimal wall shear parameter for the prediction of plaque location-A patient-specific computational study in human right coronary arteries. , 2010, Atherosclerosis.

[37]  E. Edelman,et al.  Role of endothelial shear stress in the natural history of coronary atherosclerosis and vascular remodeling: molecular, cellular, and vascular behavior. , 2007, Journal of the American College of Cardiology.

[38]  Hong Sun Ryou,et al.  Construction of healthy arteries using computed tomography and virtual histology intravascular ultrasound. , 2012, Journal of biomechanics.

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

[40]  Timothy J. Gundert,et al.  Optical Coherence Tomography for Patient-specific 3D Artery Reconstruction and Evaluation of Wall Shear Stress in a Left Circumflex Coronary Artery , 2011 .

[41]  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.

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

[43]  M. Fortin,et al.  A stable finite element for the stokes equations , 1984 .

[44]  Charles A. Taylor,et al.  Coronary Artery Axial Plaque Stress and its Relationship With Lesion Geometry: Application of Computational Fluid Dynamics to Coronary CT Angiography. , 2015, JACC. Cardiovascular imaging.

[45]  B. Chow,et al.  Can differences in corrected coronary opacification measured with computed tomography predict resting coronary artery flow? , 2011, Journal of the American College of Cardiology.