Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural network

ANN Artificial neural network CABG Coronary artery bypass graft DEIM Discrete empirical interpolation method FE Finite element FFD Free form deformation FOM Full order model FV Finite volume LAD Left anterior descending artery LCx Left circumflex artery LITA Left internal thoracic artery LMCA Left main coronary artery NURBS Non-uniform rational basis spline POD Proper orthogonal decomposition RBF Radial basis functions ROM Reduced order model SV Saphenous vein WSS Wall shear stress

[1]  Gianluigi Rozza,et al.  On optimization, control and shape design of an arterial bypass , 2005 .

[2]  Lucas O. Müller,et al.  Machine learning augmented reduced-order models for FFR-prediction , 2021 .

[3]  A. Quarteroni,et al.  Reduced Basis Methods for Partial Differential Equations: An Introduction , 2015 .

[4]  A. Quarteroni,et al.  A reduced computational and geometrical framework for inverse problems in hemodynamics , 2013, International journal for numerical methods in biomedical engineering.

[5]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[6]  Alfio Quarteroni,et al.  Boundary control and shape optimization for the robust design of bypass anastomoses under uncertainty , 2013 .

[7]  M. Comisso,et al.  A non-intrusive data-driven ROM framework for hemodynamics problems , 2020, ArXiv.

[8]  J. Hesthaven,et al.  Non-intrusive reduced order modeling of nonlinear problems using neural networks , 2018, J. Comput. Phys..

[9]  Mihailo Ristic,et al.  Measurement-based modification of NURBS surfaces , 2002, Comput. Aided Des..

[10]  Gianluigi Rozza,et al.  Finite element based model order reduction for parametrized one-way coupled steady state linear thermomechanical problems , 2021, ArXiv.

[11]  Arcot Sowmya,et al.  Deep Learning for Time Averaged Wall Shear Stress Prediction in Left Main Coronary Bifurcations , 2020, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).

[12]  Gianluigi Rozza,et al.  PyGeM: Python Geometrical Morphing , 2021, Softw. Impacts.

[13]  H. Ashrafian,et al.  Surgical patch angioplasty of the left main coronary artery. , 2012, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[14]  Gianluigi Rozza,et al.  Non-intrusive PODI-ROM for patient-specific aortic blood flow in presence of a LVAD device , 2020, Medical engineering & physics.

[15]  Manoj Kumar,et al.  A review study on blood in human coronary artery: Numerical approach , 2019, Comput. Methods Programs Biomed..

[16]  Vartan Kurtcuoglu,et al.  Reduced-order modeling of blood flow for noninvasive functional evaluation of coronary artery disease , 2019, Biomechanics and Modeling in Mechanobiology.

[17]  Gianluigi Rozza,et al.  Efficient geometrical parametrization for finite‐volume‐based reduced order methods , 2019, International Journal for Numerical Methods in Engineering.

[18]  Guang-Zhong Yang,et al.  Spiral phase velocity mapping of left and right coronary artery blood flow: Correction for through‐plane motion using selective fat‐only excitation , 2004, Journal of magnetic resonance imaging : JMRI.

[19]  A. Quarteroni,et al.  OPTIMAL CONTROL AND SHAPE OPTIMIZATION OF AORTO-CORONARIC BYPASS ANASTOMOSES , 2003 .

[20]  F. Loop,et al.  Surgery for Acquired Cardiovascular DiseaseIsolated bypass grafting of the left internal thoracic artery to the left anterior descending coronary artery: Late consequences of incomplete revascularization☆☆☆ , 2000 .

[21]  K. Kara,et al.  Cross-sectional area measurement of the coronary arteries using CT angiography at the level of the bifurcation: is there a relationship? , 2015, Diagnostic and interventional radiology.

[22]  S. Macheers,et al.  Priorities in coronary artery bypass grafting: Is midterm survival more dependent on completeness of revascularization or multiple arterial grafts? , 2019, The Journal of thoracic and cardiovascular surgery.

[23]  Gianluigi Rozza,et al.  Model Order Reduction: a survey , 2016 .

[24]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[25]  Gianluigi Rozza,et al.  A data-driven partitioned approach for the resolution of time-dependent optimal control problems with dynamic mode decomposition , 2021, ArXiv.

[26]  Gianluigi Rozza,et al.  The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations , 2021, Computer Methods in Applied Mechanics and Engineering.

[27]  P. Perdikaris,et al.  Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks , 2019 .

[28]  Henry J. Lamousin,et al.  NURBS-based free-form deformations , 1994, IEEE Computer Graphics and Applications.

[29]  Alison L. Marsden,et al.  Multiscale Modeling of Cardiovascular Flows for Clinical Decision Support , 2015 .

[30]  A. Quarteroni,et al.  Numerical modeling of hemodynamics scenarios of patient-specific coronary artery bypass grafts , 2017, Biomechanics and Modeling in Mechanobiology.

[31]  Gianluigi Rozza,et al.  Reduction Strategies for Shape Dependent Inverse Problems in Haemodynamics , 2011, System Modelling and Optimization.

[32]  Dhanjoo Ghista,et al.  Generating wall shear stress for coronary artery in real-time using neural networks: Feasibility and initial results based on idealized models , 2020, Comput. Biol. Medicine.

[33]  Gianluigi Rozza,et al.  An optimal control approach to determine resistance‐type boundary conditions from in‐vivo data for cardiovascular simulations , 2021, International journal for numerical methods in biomedical engineering.

[34]  Gianluigi Rozza,et al.  An artificial neural network approach to bifurcating phenomena in computational fluid dynamics , 2021, Computers & Fluids.

[35]  Gianluigi Rozza,et al.  Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD-Galerkin method and a vascular shape parametrization , 2016, J. Comput. Phys..

[36]  Gianluigi Rozza,et al.  A Reduced-Order Strategy for Solving Inverse Bayesian Shape Identification Problems in Physiological Flows , 2012, HPSC.

[37]  A. Quarteroni,et al.  Shape optimization for viscous flows by reduced basis methods and free‐form deformation , 2012 .

[38]  Ioannis K. Nikolos,et al.  Freeform Deformation Versus B-Spline Representation in Inverse Airfoil Design , 2008, J. Comput. Inf. Sci. Eng..

[39]  Gianluigi Rozza,et al.  Reduced order methods for parametric optimal flow control in coronary bypass grafts, toward patient‐specific data assimilation , 2019, International journal for numerical methods in biomedical engineering.

[40]  C. Leclercq,et al.  Isolated left main coronary artery stenosis: long term follow up in 106 patients after surgery , 2002, Heart.

[41]  Wei Sun,et al.  A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta. , 2019, Journal of biomechanics.

[42]  T Shimono,et al.  Mr flow measurement in the internal mammary artery-to-coronary artery bypass graft: comparison with graft stenosis at radiographic angiography. , 2001, Radiology.

[43]  Karen Willcox,et al.  A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems , 2015, SIAM Rev..

[44]  J. Hesthaven,et al.  Certified Reduced Basis Methods for Parametrized Partial Differential Equations , 2015 .

[45]  Gianluigi Rozza,et al.  An extended physics informed neural network for preliminary analysis of parametric optimal control problems , 2021, ArXiv.

[46]  Jan S. Hesthaven,et al.  Physics-informed machine learning for reduced-order modeling of nonlinear problems , 2021, J. Comput. Phys..

[47]  Martín Burgos,et al.  NURBS-Based Geometry Parameterization for Aerodynamic Shape Optimization , 2015 .

[48]  Raducanu Razvan,et al.  MATHEMATICAL MODELS and METHODS in APPLIED SCIENCES , 2012 .

[49]  A. Quarteroni,et al.  Model reduction techniques for fast blood flow simulation in parametrized geometries , 2012, International journal for numerical methods in biomedical engineering.

[50]  Anidhya Athaiya,et al.  ACTIVATION FUNCTIONS IN NEURAL NETWORKS , 2020, International Journal of Engineering Applied Sciences and Technology.

[51]  C. Eckart,et al.  The approximation of one matrix by another of lower rank , 1936 .