Kinematic boundary conditions substantially impact in silico ventricular function

Computational cardiac mechanical models, individualized to the patient, have the potential to elucidate the fundamentals of cardiac (patho-)physiology, enable non-invasive quantification of clinically significant metrics (eg, stiffness, active contraction, work), and anticipate the potential efficacy of therapeutic cardiovascular intervention. In a clinical setting, however, the available imaging resolution is often limited, which limits cardiac models to focus on the ventricles, without including the atria, valves, and proximal arteries and veins. In such models, the absence of surrounding structures needs to be accounted for by imposing realistic kinematic boundary conditions, which, for prognostic purposes, are preferably generic and thus non-image derived. Unfortunately, the literature on cardiac models shows no consistent approach to kinematically constrain the myocardium. The impact of different approaches (eg, fully constrained base, constrained epi-ring) on the predictive capacity of cardiac mechanical models has not been thoroughly studied. For that reason, this study first gives an overview of current approaches to kinematically constrain (bi) ventricular models. Next, we developed a patient-specific in silico biventricular model that compares well with literature and in vivo recorded strains. Alternative constraints were introduced to assess the influence of commonly used mechanical boundary conditions on both the predicted global functional behavior of the in-silico heart (cavity volumes, stroke volume, ejection fraction) and local strain distributions. Meaningful differences in global functioning were found between different kinematic anchoring strategies, which brought forward the importance of selecting appropriate boundary conditions for biventricular models that, in the near future, may inform clinical intervention. However, whilst statistically significant differences were also found in local strain distributions, these differences were minor and mostly confined to the region close to the applied boundary conditions.

[1]  Daniel B. Ennis,et al.  Construction and Validation of Subject-Specific Biventricular Finite-Element Models of Healthy and Failing Swine Hearts From High-Resolution DT-MRI , 2018, Front. Physiol..

[2]  Adarsh Krishnamurthy,et al.  Patient-specific models of cardiac biomechanics , 2013, J. Comput. Phys..

[3]  Thomas Franz,et al.  The effect of hydrogel injection on cardiac function and myocardial mechanics in a computational post-infarction model , 2013, Computer methods in biomechanics and biomedical engineering.

[4]  Steven Niederer,et al.  Transcatheter mitral valve replacement in mitral annulus calcification - "The art of computer simulation". , 2018, Journal of cardiovascular computed tomography.

[5]  Karl A. Tomlinson,et al.  Cardiac Microstructure: Implications for Electrical Propagation and Defibrillation in the Heart , 2002, Circulation research.

[6]  V. Wedeen,et al.  Diffusion Tensor Magnetic Resonance Imaging Mapping the Fiber Architecture Remodeling in Human Myocardium After Infarction: Correlation With Viability and Wall Motion , 2006, Circulation.

[7]  Boyce E. Griffith,et al.  Quasi-static image-based immersed boundary-finite element model of left ventricle under diastolic loading , 2014, International journal for numerical methods in biomedical engineering.

[8]  Gabriel Acevedo-Bolton,et al.  Human Cardiac Function Simulator for the Optimal Design of a Novel Annuloplasty Ring with a Sub-valvular Element for Correction of Ischemic Mitral Regurgitation , 2015, Cardiovascular engineering and technology.

[9]  N. Bruining,et al.  Patient-specific image-based computer simulation for theprediction of valve morphology and calcium displacement after TAVI with the Medtronic CoreValve and the Edwards SAPIEN valve. , 2016, EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology.

[10]  Daniel Burkhoff,et al.  Single-beat estimation of end-diastolic pressure-volume relationship: a novel method with potential for noninvasive application. , 2006, American journal of physiology. Heart and circulatory physiology.

[11]  Hervé Delingette,et al.  Toward patient-specific myocardial models of the heart. , 2008, Heart failure clinics.

[12]  T. Arts,et al.  Characterization of the normal cardiac myofiber field in goat measured with MR-diffusion tensor imaging. , 2002, American journal of physiology. Heart and circulatory physiology.

[13]  Liang Zhong,et al.  Efficient estimation of personalized biventricular mechanical function employing gradient‐based optimization , 2018, International journal for numerical methods in biomedical engineering.

[14]  M. De Craene,et al.  A Framework for the Generation of Realistic Synthetic Cardiac Ultrasound and Magnetic Resonance Imaging Sequences From the Same Virtual Patients , 2018, IEEE Transactions on Medical Imaging.

[15]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[16]  David Saloner,et al.  A computationally efficient formal optimization of regional myocardial contractility in a sheep with left ventricular aneurysm. , 2009, Journal of biomechanical engineering.

[17]  Aditya V. S. Ponnaluri,et al.  A viscoactive constitutive modeling framework with variational updates for the myocardium. , 2017, Computer methods in applied mechanics and engineering.

[18]  Gabriel Acevedo-Bolton,et al.  Distribution of normal human left ventricular myofiber stress at end diastole and end systole: a target for in silico design of heart failure treatments. , 2014, Journal of applied physiology.

[19]  G. Plank,et al.  A Novel Rule-Based Algorithm for Assigning Myocardial Fiber Orientation to Computational Heart Models , 2012, Annals of Biomedical Engineering.

[20]  T. J. Wang,et al.  A modified Holzapfel-Ogden law for a residually stressed finite strain model of the human left ventricle in diastole , 2014, Biomechanics and modeling in mechanobiology.

[21]  P F Niederer,et al.  A finite element model of the human left ventricular systole , 2006, Computer methods in biomechanics and biomedical engineering.

[22]  M. Friedberg,et al.  Right Versus Left Ventricular Failure: Differences, Similarities, and Interactions , 2014, Circulation.

[23]  Hao Gao,et al.  Parameter estimation in a Holzapfel–Ogden law for healthy myocardium , 2015, Journal of Engineering Mathematics.

[24]  Gernot Plank,et al.  Influence of myocardial fiber/sheet orientations on left ventricular mechanical contraction , 2013 .

[25]  D. Chapelle,et al.  MODELING AND ESTIMATION OF THE CARDIAC ELECTROMECHANICAL ACTIVITY , 2006 .

[26]  Mehrdad Soleimani,et al.  A coupled biventricular finite element and lumped-parameter circulatory system model of heart failure , 2013, Computer methods in biomechanics and biomedical engineering.

[27]  Litao Yan,et al.  Unsupervised reconstruction of a three‐dimensional left ventricular strain from parallel tagged cardiac images , 2003, Magnetic resonance in medicine.

[28]  Michael W Gee,et al.  A monolithic 3D‐0D coupled closed‐loop model of the heart and the vascular system: Experiment‐based parameter estimation for patient‐specific cardiac mechanics , 2017, International journal for numerical methods in biomedical engineering.

[29]  R. Ogden,et al.  Structure‐based finite strain modelling of the human left ventricle in diastole , 2013, International journal for numerical methods in biomedical engineering.

[30]  J D Humphrey,et al.  A new constitutive formulation for characterizing the mechanical behavior of soft tissues. , 1987, Biophysical journal.

[31]  J. Wong,et al.  Generating fibre orientation maps in human heart models using Poisson interpolation , 2014, Computer methods in biomechanics and biomedical engineering.

[32]  F. Yin,et al.  A multiaxial constitutive law for mammalian left ventricular myocardium in steady-state barium contracture or tetanus. , 1998, Journal of biomechanical engineering.

[33]  D. Hurtado,et al.  Computational modeling of non-linear diffusion in cardiac electrophysiology: A novel porous-medium approach , 2016 .

[34]  D Ambrosi,et al.  Active contraction of the cardiac ventricle and distortion of the microstructural architecture , 2014, International journal for numerical methods in biomedical engineering.

[35]  Theodoros N. Arvanitis,et al.  Passive diastolic modelling of human ventricles: Effects of base movement and geometrical heterogeneity. , 2017, Journal of biomechanics.

[36]  P P Lunkenheimer,et al.  Diastolic ventricular aspiration: a mechanism supporting the rapid filling phase of the human ventricles. , 2008, Journal of theoretical biology.

[37]  Eric Kerfoot,et al.  Non-invasive Model-Based Assessment of Passive Left-Ventricular Myocardial Stiffness in Healthy Subjects and in Patients with Non-ischemic Dilated Cardiomyopathy , 2016, Annals of Biomedical Engineering.

[38]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association. , 2002, Circulation.

[39]  Liang Zhong,et al.  Patient-Specific Computational Analysis of Ventricular Mechanics in Pulmonary Arterial Hypertension. , 2016, Journal of biomechanical engineering.

[40]  A. McCulloch,et al.  Modelling cardiac mechanical properties in three dimensions , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[41]  Thomas Fritz,et al.  Verification of cardiac mechanics software: benchmark problems and solutions for testing active and passive material behaviour , 2015, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[42]  Lik Chuan Lee,et al.  Applications of Computational Modeling in Cardiac Surgery , 2014, Journal of cardiac surgery.

[43]  Huafeng Liu,et al.  Meshfree implementation of individualized active cardiac dynamics , 2010, Comput. Medical Imaging Graph..

[44]  Kawal S. Rhode,et al.  The Importance of Model Parameters and Boundary Conditions in Whole Organ Models of Cardiac Contraction , 2009, FIMH.

[45]  Theodoros N. Arvanitis,et al.  Computational modelling of left-ventricular diastolic mechanics: effect of fibre orientation and right-ventricle topology. , 2015, Journal of biomechanics.

[46]  Lionel H. Opie,et al.  Heart Physiology: From Cell to Circulation , 2003 .

[47]  Steven Niederer,et al.  The Role of the Frank–Starling Law in the Transduction of Cellular Work to Whole Organ Pump Function: A Computational Modeling Analysis , 2009, PLoS Comput. Biol..

[48]  Myrianthi Hadjicharalambous,et al.  Patient-specific modeling for left ventricular mechanics using data-driven boundary energies , 2017 .

[49]  Hervé Delingette,et al.  Noname manuscript No. (will be inserted by the editor) Fast Parameter Calibration of a Cardiac Electromechanical Model from Medical Images based on the Unscented Transform , 2012 .

[50]  Gerhard A Holzapfel,et al.  Constitutive modelling of passive myocardium: a structurally based framework for material characterization , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[51]  E Kuhl,et al.  Heterogeneous growth-induced prestrain in the heart. , 2015, Journal of biomechanics.

[52]  Roy C. P. Kerckhoffs,et al.  Patient-specific modeling of dyssynchronous heart failure: a case study. , 2011, Progress in biophysics and molecular biology.

[53]  P. Libby,et al.  Braunwald's Heart Disease: A Textbook of Cardiovascular Medicine, 2-Volume Set, 9th Edition Expert Consult Premium Edition €“ Enhanced Online Features , 2011 .

[54]  Alfio Quarteroni,et al.  Numerical approximation of the electromechanical coupling in the left ventricle with inclusion of the Purkinje network , 2018, International journal for numerical methods in biomedical engineering.

[55]  S. Göktepe,et al.  Electromechanics of the heart: a unified approach to the strongly coupled excitation–contraction problem , 2010 .

[56]  Gabriel Acevedo-Bolton,et al.  A Novel Method for Quantifying Smooth Regional Variations in Myocardial Contractility Within an Infarcted Human Left Ventricle Based on Delay-Enhanced Magnetic Resonance Imaging. , 2015, Journal of biomechanical engineering.

[57]  Steven Deutsch,et al.  Assessment of CFD Performance in Simulations of an Idealized Medical Device: Results of FDA’s First Computational Interlaboratory Study , 2012 .

[58]  Serdar Göktepe,et al.  Computational modeling of coupled cardiac electromechanics incorporating cardiac dysfunctions , 2014 .

[59]  Hans Torp,et al.  Myocardial strain imaging: how useful is it in clinical decision making? , 2015, European heart journal.

[60]  A. McCulloch,et al.  Relating myocardial laminar architecture to shear strain and muscle fiber orientation. , 2001, American journal of physiology. Heart and circulatory physiology.

[61]  Jan D'hooge,et al.  Cardiac Motion and Deformation Estimation from Tagged MRI Sequences Using a Temporal Coherent Image Registration Framework , 2013, FIMH.

[62]  Gabriel Acevedo-Bolton,et al.  Partial LVAD restores ventricular outputs and normalizes LV but not RV stress distributions in the acutely failing heart in silico , 2016, The International journal of artificial organs.

[63]  Jay D. Humphrey,et al.  Review Paper: Continuum biomechanics of soft biological tissues , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[64]  Raimond Winslow,et al.  Whole-Heart Modeling Applications to Cardiac Electrophysiology and Electromechanics , 2017 .

[65]  D. Bluemke,et al.  Normal values for cardiovascular magnetic resonance in adults and children , 2015, Journal of Cardiovascular Magnetic Resonance.

[66]  N. Trayanova,et al.  Advances in modeling ventricular arrhythmias: from mechanisms to the clinic , 2014, Wiley interdisciplinary reviews. Systems biology and medicine.

[67]  G. Holzapfel,et al.  An orthotropic viscoelastic model for the passive myocardium: continuum basis and numerical treatment , 2016, Computer methods in biomechanics and biomedical engineering.

[68]  Alfio Quarteroni,et al.  Integrated Heart—Coupling multiscale and multiphysics models for the simulation of the cardiac function , 2017 .

[69]  Patrick Segers,et al.  A modular inverse elastostatics approach to resolve the pressure-induced stress state for in vivo imaging based cardiovascular modeling. , 2018, Journal of the mechanical behavior of biomedical materials.

[70]  Andrew D McCulloch,et al.  Laminar fiber architecture and three-dimensional systolic mechanics in canine ventricular myocardium. , 1999, American journal of physiology. Heart and circulatory physiology.

[71]  Pablo Lamata,et al.  An accurate, fast and robust method to generate patient-specific cubic Hermite meshes , 2011, Medical Image Anal..

[72]  Gui-Rong Liu,et al.  A smoothed finite element method for analysis of anisotropic large deformation of passive rabbit ventricles in diastole , 2015, International journal for numerical methods in biomedical engineering.

[73]  Patrick Clarysse,et al.  Characterization of normal regional myocardial function by MRI cardiac tagging , 2015, Journal of magnetic resonance imaging : JMRI.

[74]  Hervé Delingette,et al.  Human Atlas of the Cardiac Fiber Architecture: Study on a Healthy Population , 2012, IEEE Transactions on Medical Imaging.

[75]  C. Bucciarelli-Ducci,et al.  Strain imaging using cardiac magnetic resonance , 2017, Heart Failure Reviews.

[76]  Pablo Lamata,et al.  Images as drivers of progress in cardiac computational modelling , 2014, Progress in biophysics and molecular biology.

[77]  David A Bluemke,et al.  Regional myocardial functional patterns: Quantitative tagged magnetic resonance imaging in an adult population free of cardiovascular risk factors: The multi‐ethnic study of atherosclerosis (MESA) , 2015, Journal of magnetic resonance imaging : JMRI.

[78]  J. Guccione,et al.  MRI-based finite-element analysis of left ventricular aneurysm. , 2005, American journal of physiology. Heart and circulatory physiology.

[79]  Pablo Lamata,et al.  A computational pipeline for quantification of mouse myocardial stiffness parameters , 2014, Comput. Biol. Medicine.

[80]  R Fumero,et al.  The cardiac torsion as a sensitive index of heart pathology: A model study. , 2016, Journal of the mechanical behavior of biomedical materials.

[81]  Kevin L. Sack,et al.  POPULATION-BASED MYOFIBRE ASSIGNMENT: THE IMPORTANCE OF LONGITUDINAL VARIATION IN SUBJECT-SPECIFIC CARDIAC MODELS , 2016 .

[82]  M. Sacks Biaxial Mechanical Evaluation of Planar Biological Materials , 2000 .

[83]  P. Segers,et al.  Patient-Specific Computer Simulation to Elucidate the Role of Contact Pressure in the Development of New Conduction Abnormalities After Catheter-Based Implantation of a Self-Expanding Aortic Valve , 2018, Circulation. Cardiovascular interventions.

[84]  J. Hurlé,et al.  Myocardial fiber architecture of the human heart ventricles , 1982, The Anatomical record.

[85]  Jan D'hooge,et al.  Strain rate imaging: fundamental principles and progress so far , 2010 .

[86]  N. Bruining,et al.  Patient-Specific Computer Modeling to Predict Aortic Regurgitation After Transcatheter Aortic Valve Replacement. , 2016, JACC. Cardiovascular interventions.

[87]  Kevin L. Sack,et al.  Personalised computational cardiology: Patient-specific modelling in cardiac mechanics and biomaterial injection therapies for myocardial infarction , 2016, Heart Failure Reviews.

[88]  M. Cerqueira,et al.  Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: A statement for healthcare professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association , 2002, The international journal of cardiovascular imaging.

[89]  Jan D’hooge,et al.  Cardiovascular magnetic resonance myocardial feature tracking using a non-rigid, elastic image registration algorithm: assessment of variability in a real-life clinical setting , 2017, Journal of Cardiovascular Magnetic Resonance.

[90]  Hervé Delingette,et al.  Statistical Analysis of the Human Cardiac Fiber Architecture from DT-MRI , 2011, FIMH.

[91]  Alfio Quarteroni,et al.  A monolithic algorithm for the simulation of cardiac electromechanics in the human left ventricle , 2018 .

[92]  Giovanni Biglino,et al.  Computational modelling for congenital heart disease: how far are we from clinical translation? , 2016, Heart.

[93]  Ellen Kuhl,et al.  The Living Heart Project: A robust and integrative simulator for human heart function. , 2014, European journal of mechanics. A, Solids.