1
|
Genet M, Diaz J, Chapelle D, Moireau P. Reduced left ventricular dynamics modeling based on a cylindrical assumption. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2023; 39:e3711. [PMID: 37203282 DOI: 10.1002/cnm.3711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 02/11/2023] [Accepted: 04/02/2023] [Indexed: 05/20/2023]
Abstract
Biomechanical modeling and simulation is expected to play a significant role in the development of the next generation tools in many fields of medicine. However, full-order finite element models of complex organs such as the heart can be computationally very expensive, thus limiting their practical usability. Therefore, reduced models are much valuable to be used, for example, for pre-calibration of full-order models, fast predictions, real-time applications, and so forth. In this work, focused on the left ventricle, we develop a reduced model by defining reduced geometry & kinematics while keeping general motion and behavior laws, allowing to derive a reduced model where all variables & parameters have a strong physical meaning. More specifically, we propose a reduced ventricular model based on cylindrical geometry & kinematics, which allows to describe the myofiber orientation through the ventricular wall and to represent contraction patterns such as ventricular twist, two important features of ventricular mechanics. Our model is based on the original cylindrical model of Guccione, McCulloch, & Waldman (1991); Guccione, Waldman, & McCulloch (1993), albeit with multiple differences: we propose a fully dynamical formulation, integrated into an open-loop lumped circulation model, and based on a material behavior that incorporates a fine description of contraction mechanisms; moreover, the issue of the cylinder closure has been completely reformulated; our numerical approach is novel aswell, with consistent spatial (finite element) and time discretizations. Finally, we analyze the sensitivity of the model response to various numerical and physical parameters, and study its physiological response.
Collapse
Affiliation(s)
- Martin Genet
- LMS, École Polytechnique/CNRS/Institut Polytechnique de Paris, Palaiseau, France
- Inria, MΞDISIM Team, Inria Saclay-Ile de France, Palaiseau, France
| | - Jérôme Diaz
- LMS, École Polytechnique/CNRS/Institut Polytechnique de Paris, Palaiseau, France
- Inria, MΞDISIM Team, Inria Saclay-Ile de France, Palaiseau, France
| | - Dominique Chapelle
- LMS, École Polytechnique/CNRS/Institut Polytechnique de Paris, Palaiseau, France
- Inria, MΞDISIM Team, Inria Saclay-Ile de France, Palaiseau, France
| | - Philippe Moireau
- LMS, École Polytechnique/CNRS/Institut Polytechnique de Paris, Palaiseau, France
- Inria, MΞDISIM Team, Inria Saclay-Ile de France, Palaiseau, France
| |
Collapse
|
2
|
Chapelle D, Le Gall A. A biomechanics-based parametrized cardiac end-diastolic pressure-volume relationship for accurate patient-specific calibration and estimation. Sci Rep 2023; 13:11232. [PMID: 37433813 DOI: 10.1038/s41598-023-38196-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/05/2023] [Indexed: 07/13/2023] Open
Abstract
A simple power law has been proposed in the pioneering work of Klotz et al. (Am J Physiol Heart Circ Physiol 291(1):H403-H412, 2006) to approximate the end-diastolic pressure-volume relationship of the left cardiac ventricle, with limited inter-individual variability provided the volume is adequately normalized. Nevertheless, we use here a biomechanical model to investigate the sources of the remaining data dispersion observed in the normalized space, and we show that variations of the parameters of the biomechanical model realistically account for a substantial part of this dispersion. We therefore propose an alternative law based on the biomechanical model that embeds some intrinsic physical parameters, which directly enables personalization capabilities, and paves the way for related estimation approaches.
Collapse
Affiliation(s)
- Dominique Chapelle
- Inria, Palaiseau, France.
- LMS, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France.
| | - Arthur Le Gall
- Inria, Palaiseau, France
- LMS, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
- AP-HP, Hôpital Lariboisière, Paris, France
| |
Collapse
|
3
|
Schwarz EL, Pegolotti L, Pfaller MR, Marsden AL. Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease. BIOPHYSICS REVIEWS 2023; 4:011301. [PMID: 36686891 PMCID: PMC9846834 DOI: 10.1063/5.0109400] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/12/2022] [Indexed: 01/15/2023]
Abstract
Physics-based computational models of the cardiovascular system are increasingly used to simulate hemodynamics, tissue mechanics, and physiology in evolving healthy and diseased states. While predictive models using computational fluid dynamics (CFD) originated primarily for use in surgical planning, their application now extends well beyond this purpose. In this review, we describe an increasingly wide range of modeling applications aimed at uncovering fundamental mechanisms of disease progression and development, performing model-guided design, and generating testable hypotheses to drive targeted experiments. Increasingly, models are incorporating multiple physical processes spanning a wide range of time and length scales in the heart and vasculature. With these expanded capabilities, clinical adoption of patient-specific modeling in congenital and acquired cardiovascular disease is also increasing, impacting clinical care and treatment decisions in complex congenital heart disease, coronary artery disease, vascular surgery, pulmonary artery disease, and medical device design. In support of these efforts, we discuss recent advances in modeling methodology, which are most impactful when driven by clinical needs. We describe pivotal recent developments in image processing, fluid-structure interaction, modeling under uncertainty, and reduced order modeling to enable simulations in clinically relevant timeframes. In all these areas, we argue that traditional CFD alone is insufficient to tackle increasingly complex clinical and biological problems across scales and systems. Rather, CFD should be coupled with appropriate multiscale biological, physical, and physiological models needed to produce comprehensive, impactful models of mechanobiological systems and complex clinical scenarios. With this perspective, we finally outline open problems and future challenges in the field.
Collapse
Affiliation(s)
- Erica L. Schwarz
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Luca Pegolotti
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Martin R. Pfaller
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Alison L. Marsden
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| |
Collapse
|
4
|
Gusseva M, Castellanos DA, Greer JS, Hussein MA, Hasbani K, Greil G, Veeram Reddy SR, Hussain T, Chapelle D, Chabiniok R. Time-Synchronization of Interventional Cardiovascular Magnetic Resonance Data Using a Biomechanical Model for Pressure-Volume Loop Analysis. J Magn Reson Imaging 2023; 57:320-323. [PMID: 35567583 DOI: 10.1002/jmri.28216] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 04/15/2022] [Indexed: 02/04/2023] Open
Affiliation(s)
- Maria Gusseva
- Inria, Palaiseau, France.,LMS, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
| | - Daniel A Castellanos
- Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Joshua S Greer
- Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Mohamed Abdelghafar Hussein
- Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA.,Pediatric Department, Kafrelsheikh University, Kafr Elsheikh, Egypt
| | - Keren Hasbani
- Division of Pediatric Cardiology, Department of Pediatrics, Dell Medical School, UT Austin, Texas, USA
| | - Gerald Greil
- Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Surendranath R Veeram Reddy
- Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Tarique Hussain
- Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Dominique Chapelle
- Inria, Palaiseau, France.,LMS, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
| | - Radomír Chabiniok
- Inria, Palaiseau, France.,LMS, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France.,Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA.,Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| |
Collapse
|
5
|
A quasi-static poromechanical model of the lungs. Biomech Model Mechanobiol 2022; 21:527-551. [DOI: 10.1007/s10237-021-01547-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 12/09/2021] [Indexed: 11/02/2022]
|
6
|
Prediction of Ventricular Mechanics After Pulmonary Valve Replacement in Tetralogy of Fallot by Biomechanical Modeling: A Step Towards Precision Healthcare. Ann Biomed Eng 2021; 49:3339-3348. [PMID: 34853921 DOI: 10.1007/s10439-021-02895-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/12/2021] [Indexed: 10/19/2022]
Abstract
Clinical indicators of heart function are often limited in their ability to accurately evaluate the current mechanical state of the myocardium. Biomechanical modeling has been shown to be a promising tool in addition to clinical indicators. By providing a patient-specific measure of myocardial active stress (contractility), biomechanical modeling can enhance the precision of the description of patient's pathophysiology at any given point in time. In this work we aim to explore the ability of biomechanical modeling to predict the response of ventricular mechanics to the progressively decreasing afterload in repaired tetralogy of Fallot (rTOF) patients undergoing pulmonary valve replacement (PVR) for significant residual right ventricular outflow tract obstruction (RVOTO). We used 19 patient-specific models of patients with rTOF prior to pulmonary valve replacement (PVR), denoted as PSMpre, and patient-specific models of the same patients created post-PVR (PSMpost)-both created in our previous published work. Using the PSMpre and assuming cessation of the pulmonary regurgitation and a progressive decrease of RVOT resistance, we built relationships between the contractility and RVOT resistance post-PVR. The predictive value of such in silico obtained relationships were tested against the PSMpost, i.e. the models created from the actual post-PVR datasets. Our results show a linear 1-dimensional relationship between the in silico predicted contractility post-PVR and the RVOT resistance. The predicted contractility was close to the contractility in the PSMpost model with a mean (± SD) difference of 6.5 (± 3.0)%. The relationships between the contractility predicted by in silico PVR vs. RVOT resistance have a potential to inform clinicians about hypothetical mechanical response of the ventricle based on the degree of pre-operative RVOTO.
Collapse
|
7
|
Zaman MS, Dhamala J, Bajracharya P, Sapp JL, Horácek BM, Wu KC, Trayanova NA, Wang L. Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning. Front Physiol 2021; 12:740306. [PMID: 34759835 PMCID: PMC8573318 DOI: 10.3389/fphys.2021.740306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/24/2021] [Indexed: 11/13/2022] Open
Abstract
Probabilistic estimation of cardiac electrophysiological model parameters serves an important step toward model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes direct Markov Chain Monte Carlo (MCMC) sampling of the posterior probability density function (pdf) of model parameters computationally intensive. Approximated posterior pdfs resulting from replacing the simulation model with a computationally efficient surrogate, on the other hand, have seen limited accuracy. In this study, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh. We further introduce new acquisition functions to focus the selection of training points on better approximating the shape rather than the modes of the posterior pdf of interest. We evaluated the presented method in estimating tissue excitability in a 3D cardiac electrophysiological model in a range of synthetic and real-data experiments. We demonstrated its improved accuracy in approximating the posterior pdf compared to Bayesian active learning using regular acquisition functions, and substantially reduced computational cost in comparison to existing standard or accelerated MCMC sampling.
Collapse
Affiliation(s)
- Md Shakil Zaman
- Rochester Institute of Technology, Rochester, NY, United States
| | - Jwala Dhamala
- Rochester Institute of Technology, Rochester, NY, United States
| | | | - John L Sapp
- Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - B Milan Horácek
- Department of Electrical and Computer Engineering, Halifax, NS, Canada
| | - Katherine C Wu
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Linwei Wang
- Rochester Institute of Technology, Rochester, NY, United States
| |
Collapse
|
8
|
Gusseva M, Hussain T, Friesen CH, Moireau P, Tandon A, Patte C, Genet M, Hasbani K, Greil G, Chapelle D, Chabiniok R. Biomechanical Modeling to Inform Pulmonary Valve Replacement in Tetralogy of Fallot Patients After Complete Repair. Can J Cardiol 2021; 37:1798-1807. [PMID: 34216743 PMCID: PMC9810481 DOI: 10.1016/j.cjca.2021.06.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 06/05/2021] [Accepted: 06/26/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND A biomechanical model of the heart can be used to incorporate multiple data sources (electrocardiography, imaging, invasive hemodynamics). The purpose of this study was to use this approach in a cohort of patients with tetralogy of Fallot after complete repair (rTOF) to assess comparative influences of residual right ventricular outflow tract obstruction (RVOTO) and pulmonary regurgitation on ventricular health. METHODS Twenty patients with rTOF who underwent percutaneous pulmonary valve replacement (PVR) and cardiovascular magnetic resonance imaging were included in this retrospective study. Biomechanical models specific to individual patient and physiology (before and after PVR) were created and used to estimate the RV myocardial contractility. The ability of models to capture post-PVR changes of right ventricular (RV) end-diastolic volume (EDV) and effective flow in the pulmonary artery (Qeff) was also compared with expected values. RESULTS RV contractility before PVR (mean 66 ± 16 kPa, mean ± standard deviation) was increased in patients with rTOF compared with normal RV (38-48 kPa) (P < 0.05). The contractility decreased significantly in all patients after PVR (P < 0.05). Patients with predominantly RVOTO demonstrated greater reduction in contractility (median decrease 35%) after PVR than those with predominant pulmonary regurgitation (median decrease 11%). The model simulated post-PVR decreased EDV for the majority and suggested an increase of Qeff-both in line with published data. CONCLUSIONS This study used a biomechanical model to synthesize multiple clinical inputs and give an insight into RV health. Individualized modeling allows us to predict the RV response to PVR. Initial data suggest that residual RVOTO imposes greater ventricular work than isolated pulmonary regurgitation.
Collapse
Affiliation(s)
- Maria Gusseva
- Inria, Palaiseau, France,LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
| | - Tarique Hussain
- Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Camille Hancock Friesen
- Division of Pediatric Cardiothoracic Surgery, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Philippe Moireau
- Inria, Palaiseau, France,LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
| | - Animesh Tandon
- Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Cécile Patte
- Inria, Palaiseau, France,LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
| | - Martin Genet
- Inria, Palaiseau, France,LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
| | - Keren Hasbani
- Division of Pediatric Cardiology, Department of Pediatrics, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Gerald Greil
- Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Dominique Chapelle
- Inria, Palaiseau, France,LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France
| | - Radomír Chabiniok
- Inria, Palaiseau, France,LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Palaiseau, France,Division of Pediatric Cardiology, Department of Pediatrics, UT Southwestern Medical Center, Dallas, Texas, USA,School of Biomedical Engineering & Imaging Sciences, St Thomas’ Hospital, King’s College London, London, United Kingdom,Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| |
Collapse
|
9
|
Salvador M, Fedele M, Africa PC, Sung E, Dede' L, Prakosa A, Chrispin J, Trayanova N, Quarteroni A. Electromechanical modeling of human ventricles with ischemic cardiomyopathy: numerical simulations in sinus rhythm and under arrhythmia. Comput Biol Med 2021; 136:104674. [PMID: 34340126 DOI: 10.1016/j.compbiomed.2021.104674] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 12/16/2022]
Abstract
We developed a novel patient-specific computational model for the numerical simulation of ventricular electromechanics in patients with ischemic cardiomyopathy (ICM). This model reproduces the activity both in sinus rhythm (SR) and in ventricular tachycardia (VT). The presence of scars, grey zones and non-remodeled regions of the myocardium is accounted for by the introduction of a spatially heterogeneous coefficient in the 3D electromechanics model. This 3D electromechanics model is firstly coupled with a 2-element Windkessel afterload model to fit the pressure-volume (PV) loop of a patient-specific left ventricle (LV) with ICM in SR. Then, we employ the coupling with a 0D closed-loop circulation model to analyze a VT circuit over multiple heartbeats on the same LV. We highlight similarities and differences on the solutions obtained by the electrophysiology model and those of the electromechanics model, while considering different scenarios for the circulatory system. We observe that very different parametrizations of the circulation model induce the same hemodynamical considerations for the patient at hand. Specifically, we classify this VT as unstable. We conclude by stressing the importance of combining electrophysiological, mechanical and hemodynamical models to provide relevant clinical indicators in how arrhythmias evolve and can potentially lead to sudden cardiac death.
Collapse
Affiliation(s)
- Matteo Salvador
- MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy.
| | - Marco Fedele
- MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | | | - Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Luca Dede'
- MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Adityo Prakosa
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Natalia Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Alfio Quarteroni
- MOX-Dipartimento di Matematica, Politecnico di Milano, Milan, Italy; École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| |
Collapse
|
10
|
Banus J, Lorenzi M, Camara O, Sermesant M. Biophysics-based statistical learning: Application to heart and brain interactions. Med Image Anal 2021; 72:102089. [PMID: 34020082 DOI: 10.1016/j.media.2021.102089] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 03/01/2021] [Accepted: 04/18/2021] [Indexed: 11/18/2022]
Abstract
Initiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of the cardiovascular function, such as cardiac contractility, cannot be measured in-vivo. While these non-observable parameters can be estimated by means of biophysical models, their personalisation is generally an ill-posed problem, often lacking critical data and only applied to small datasets. Therefore, to jointly study brain and heart, we propose an approach in which the parameter personalisation of a lumped cardiovascular model is constrained by the statistical relationships observed between model parameters and brain-volumetric indices extracted from imaging, i.e. ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We explored the plausibility of the learnt relationships by inferring the model parameters conditioned on the absence of part of the target clinical features, applying this framework in a cohort of more than 3 000 subjects and in a pathological subgroup of 59 subjects diagnosed with atrial fibrillation. Our results demonstrate the impact of such external features in the cardiovascular model personalisation by learning more informative parameter-space constraints. Moreover, physiologically plausible mechanisms are captured through these personalised models as well as significant differences associated to specific clinical conditions.
Collapse
Affiliation(s)
- Jaume Banus
- Université Côte d'Azur, INRIA Sophia Antipolis, Epione Project-Team, France.
| | - Marco Lorenzi
- Université Côte d'Azur, INRIA Sophia Antipolis, Epione Project-Team, France
| | - Oscar Camara
- PhySense group, BCN-MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Maxime Sermesant
- Université Côte d'Azur, INRIA Sophia Antipolis, Epione Project-Team, France
| |
Collapse
|
11
|
Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks. Med Image Anal 2021; 71:102066. [PMID: 33951597 DOI: 10.1016/j.media.2021.102066] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 03/30/2021] [Accepted: 04/01/2021] [Indexed: 11/21/2022]
Abstract
We present a parametric physics-informed neural network for the simulation of personalised left-ventricular biomechanics. The neural network is constrained to the biophysical problem in two ways: (i) the network output is restricted to a subspace built from radial basis functions capturing characteristic deformations of left ventricles and (ii) the cost function used for training is the energy potential functional specifically tailored for hyperelastic, anisotropic, nearly-incompressible active materials. The radial bases are generated from the results of a nonlinear Finite Element model coupled with an anatomical shape model derived from high-resolution cardiac images. We show that, by coupling the neural network with a simplified circulation model, we can efficiently generate computationally inexpensive estimations of cardiac mechanics. Our model is 30 times faster than the reference Finite Element model used, including training time, while yielding satisfactory average errors in the predictions of ejection fraction (-3%), peak systolic pressure (7%), stroke work (4%) and myocardial strains (14%). This physics-informed neural network is well suited to efficiently augment cardiac images with functional data and to generate large sets of synthetic cases for training deep network classifiers while it provides efficient personalization to the specific patient of interest with a high level of detail.
Collapse
|
12
|
Microstructural deformation observed by Mueller polarimetry during traction assay on myocardium samples. Sci Rep 2020; 10:20531. [PMID: 33239670 PMCID: PMC7688642 DOI: 10.1038/s41598-020-76820-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 10/26/2020] [Indexed: 11/08/2022] Open
Abstract
Despite recent advances, the myocardial microstructure remains imperfectly understood. In particular, bundles of cardiomyocytes have been observed but their three-dimensional organisation remains debated and the associated mechanical consequences unknown. One of the major challenges remains to perform multiscale observations of the mechanical response of the heart wall. For this purpose, in this study, a full-field Mueller polarimetric imager (MPI) was combined, for the first time, with an in-situ traction device. The full-field MPI enables to obtain a macroscopic image of the explored tissue, while providing detailed information about its structure on a microscopic scale. Specifically it exploits the polarization of the light to determine various biophysical quantities related to the tissue scattering or anisotropy properties. Combined with a mechanical traction device, the full-field MPI allows to measure the evolution of such biophysical quantities during tissue stretch. We observe separation lines on the tissue, which are associated with a fast variation of the fiber orientation, and have the size of cardiomyocyte bundles. Thus, we hypothesize that these lines are the perimysium, the collagen layer surrounding these bundles. During the mechanical traction, we observe two mechanisms simultaneously. On one hand, the azimuth shows an affine behavior, meaning the orientation changes according to the tissue deformation, and showing coherence in the tissue. On the other hand, the separation lines appear to be resistant in shear and compression but weak against traction, with a forming of gaps in the tissue.
Collapse
|
13
|
Kimmig F, Caruel M. Hierarchical modeling of force generation in cardiac muscle. Biomech Model Mechanobiol 2020; 19:2567-2601. [PMID: 32681201 DOI: 10.1007/s10237-020-01357-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Accepted: 06/10/2020] [Indexed: 11/25/2022]
Abstract
Performing physiologically relevant simulations of the beating heart in clinical context requires to develop detailed models of the microscale force generation process. These models, however, may reveal difficult to implement in practice due to their high computational costs and complex calibration. We propose a hierarchy of three interconnected muscle contraction models-from the more refined to the more simplified-that are rigorously and systematically related to each other, offering a way to select, for a specific application, the model that yields a good trade-off between physiological fidelity, computational cost and calibration complexity. The three model families are compared to the same set of experimental data to systematically assess what physiological indicators can be reproduced or not and how these indicators constrain the model parameters. Finally, we discuss the applicability of these models for heart simulation.
Collapse
Affiliation(s)
- François Kimmig
- LMS, CNRS, École polytechnique, Institut Polytechnique de Paris, Paris, France.
- Inria, Inria Saclay-Ile-de-France, Palaiseau, France.
| | | |
Collapse
|
14
|
Le Gall A, Vallée F, Pushparajah K, Hussain T, Mebazaa A, Chapelle D, Gayat É, Chabiniok R. Monitoring of cardiovascular physiology augmented by a patient-specific biomechanical model during general anesthesia. A proof of concept study. PLoS One 2020; 15:e0232830. [PMID: 32407353 PMCID: PMC7224549 DOI: 10.1371/journal.pone.0232830] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 04/22/2020] [Indexed: 12/29/2022] Open
Abstract
During general anesthesia (GA), direct analysis of arterial pressure or aortic flow waveforms may be inconclusive in complex situations. Patient-specific biomechanical models, based on data obtained during GA and capable to perform fast simulations of cardiac cycles, have the potential to augment hemodynamic monitoring. Such models allow to simulate Pressure-Volume (PV) loops and estimate functional indicators of cardiovascular (CV) system, e.g. ventricular-arterial coupling (Vva), cardiac efficiency (CE) or myocardial contractility, evolving throughout GA. In this prospective observational study, we created patient-specific biomechanical models of heart and vasculature of a reduced geometric complexity for n = 45 patients undergoing GA, while using transthoracic echocardiography and aortic pressure and flow signals acquired in the beginning of GA (baseline condition). If intraoperative hypotension (IOH) appeared, diluted norepinephrine (NOR) was administered and the model readjusted according to the measured aortic pressure and flow signals. Such patients were a posteriori assigned into a so-called hypotensive group. The accuracy of simulated mean aortic pressure (MAP) and stroke volume (SV) at baseline were in accordance with the guidelines for the validation of new devices or reference measurement methods in all patients. After NOR administration in the hypotensive group, the percentage of concordance with 10% exclusion zone between measurement and simulation was >95% for both MAP and SV. The modeling results showed a decreased Vva (0.64±0.37 vs 0.88±0.43; p = 0.039) and an increased CE (0.8±0.1 vs 0.73±0.11; p = 0.042) in hypotensive vs normotensive patients. Furthermore, Vva increased by 92±101%, CE decreased by 13±11% (p < 0.001 for both) and contractility increased by 14±11% (p = 0.002) in the hypotensive group post-NOR administration. In this work we demonstrated the application of fast-running patient-specific biophysical models to estimate PV loops and functional indicators of CV system using clinical data available during GA. The work paves the way for model-augmented hemodynamic monitoring at operating theatres or intensive care units to enhance the information on patient-specific physiology.
Collapse
Affiliation(s)
- Arthur Le Gall
- Inria, Paris, France
- LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Paris, France
- Anesthesiology and Intensive Care Department, Lariboisière - Saint Louis - Fernand Widal University Hospitals, Paris, France
- INSERM, Paris, France
| | - Fabrice Vallée
- Inria, Paris, France
- LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Paris, France
- Anesthesiology and Intensive Care Department, Lariboisière - Saint Louis - Fernand Widal University Hospitals, Paris, France
- INSERM, Paris, France
| | - Kuberan Pushparajah
- School of Biomedical Engineering & Imaging Sciences, St Thomas’ Hospital, King’s College London, London, United Kingdom
| | - Tarique Hussain
- Department of Pediatrics, Division of Pediatric Cardiology, UT Southwestern Medical Center, Dallas, TX, United States of America
| | - Alexandre Mebazaa
- Anesthesiology and Intensive Care Department, Lariboisière - Saint Louis - Fernand Widal University Hospitals, Paris, France
- INSERM, Paris, France
| | - Dominique Chapelle
- Inria, Paris, France
- LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Paris, France
| | - Étienne Gayat
- Anesthesiology and Intensive Care Department, Lariboisière - Saint Louis - Fernand Widal University Hospitals, Paris, France
- INSERM, Paris, France
| | - Radomír Chabiniok
- Inria, Paris, France
- LMS, École Polytechnique, CNRS, Institut Polytechnique de Paris, Paris, France
- School of Biomedical Engineering & Imaging Sciences, St Thomas’ Hospital, King’s College London, London, United Kingdom
- Department of Mathematics, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| |
Collapse
|
15
|
Pfaller MR, Cruz Varona M, Lang J, Bertoglio C, Wall WA. Using parametric model order reduction for inverse analysis of large nonlinear cardiac simulations. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2020; 36:e3320. [PMID: 32022424 DOI: 10.1002/cnm.3320] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 10/29/2019] [Accepted: 01/25/2020] [Indexed: 06/10/2023]
Abstract
Predictive high-fidelity finite element simulations of human cardiac mechanics commonly require a large number of structural degrees of freedom. Additionally, these models are often coupled with lumped-parameter models of hemodynamics. High computational demands, however, slow down model calibration and therefore limit the use of cardiac simulations in clinical practice. As cardiac models rely on several patient-specific parameters, just one solution corresponding to one specific parameter set does not at all meet clinical demands. Moreover, while solving the nonlinear problem, 90% of the computation time is spent solving linear systems of equations. We propose to reduce the structural dimension of a monolithically coupled structure-Windkessel system by projection onto a lower-dimensional subspace. We obtain a good approximation of the displacement field as well as of key scalar cardiac outputs even with very few reduced degrees of freedom, while achieving considerable speedups. For subspace generation, we use proper orthogonal decomposition of displacement snapshots. Following a brief comparison of subspace interpolation methods, we demonstrate how projection-based model order reduction can be easily integrated into a gradient-based optimization. We demonstrate the performance of our method in a real-world multivariate inverse analysis scenario. Using the presented projection-based model order reduction approach can significantly speed up model personalization and could be used for many-query tasks in a clinical setting.
Collapse
Affiliation(s)
- M R Pfaller
- Institute for Computational Mechanics, Technical University of Munich, Garching b. München, Germany
| | - M Cruz Varona
- Chair of Automatic Control, Technical University of Munich, Garching b. München, Germany
| | - J Lang
- Institute for Computational Mechanics, Technical University of Munich, Garching b. München, Germany
| | - C Bertoglio
- Bernoulli Institute, University of Groningen, AG Groningen, The Netherlands
| | - W A Wall
- Institute for Computational Mechanics, Technical University of Munich, Garching b. München, Germany
| |
Collapse
|
16
|
This A, Boilevin-Kayl L, Fernández MA, Gerbeau JF. Augmented resistive immersed surfaces valve model for the simulation of cardiac hemodynamics with isovolumetric phases. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2020; 36:e3223. [PMID: 31206245 DOI: 10.1002/cnm.3223] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 04/12/2019] [Accepted: 05/21/2019] [Indexed: 06/09/2023]
Abstract
In order to reduce the complexity of heart hemodynamics simulations, uncoupling approaches are often considered for the modeling of the immersed valves as an alternative to complex fluid-structure interaction (FSI) models. A possible shortcoming of these simplified approaches is the difficulty to correctly capture the pressure dynamics during the isovolumetric phases. In this work, we propose an enhanced resistive immersed surfaces (RIS) model of cardiac valves, which overcomes this issue. The benefits of the model are investigated and tested in blood flow simulations of the left heart where the physiological behavior of the intracavity pressure during the isovolumetric phases is recovered without using fully coupled fluid-structure models and without important alteration of the associated velocity field.
Collapse
Affiliation(s)
- Alexandre This
- Medisys, Philips Research, Suresnes, France
- COMMEDIA, Inria Paris, Paris, France
- Sorbonne Université, UMR 7598 LJLL, Paris, France
| | - Ludovic Boilevin-Kayl
- COMMEDIA, Inria Paris, Paris, France
- Sorbonne Université, UMR 7598 LJLL, Paris, France
| | - Miguel A Fernández
- COMMEDIA, Inria Paris, Paris, France
- Sorbonne Université, UMR 7598 LJLL, Paris, France
| | - Jean-Frédéric Gerbeau
- COMMEDIA, Inria Paris, Paris, France
- Sorbonne Université, UMR 7598 LJLL, Paris, France
| |
Collapse
|
17
|
Dobutamine stress testing in patients with Fontan circulation augmented by biomechanical modeling. PLoS One 2020; 15:e0229015. [PMID: 32084180 PMCID: PMC7034893 DOI: 10.1371/journal.pone.0229015] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 01/28/2020] [Indexed: 02/02/2023] Open
Abstract
Understanding (patho)physiological phenomena and mechanisms of failure in patients with Fontan circulation-a surgically established circulation for patients born with a functionally single ventricle-remains challenging due to the complex hemodynamics and high inter-patient variations in anatomy and function. In this work, we present a biomechanical model of the heart and circulation to augment the diagnostic evaluation of Fontan patients with early-stage heart failure. The proposed framework employs a reduced-order model of heart coupled with a simplified circulation including venous return, creating a closed-loop system. We deploy this framework to augment the information from data obtained during combined cardiac catheterization and magnetic resonance exams (XMR), performed at rest and during dobutamine stress in 9 children with Fontan circulation and 2 biventricular controls. We demonstrate that our modeling framework enables patient-specific investigation of myocardial stiffness, contractility at rest, contractile reserve during stress and changes in vascular resistance. Hereby, the model allows to identify key factors underlying the pathophysiological response to stress in these patients. In addition, the rapid personalization of the model to patient data and fast simulation of cardiac cycles make our framework directly applicable in a clinical workflow. We conclude that the proposed modeling framework is a valuable addition to the current clinical diagnostic XMR exam that helps to explain patient-specific stress hemodynamics and can identify potential mechanisms of failure in patients with Fontan circulation.
Collapse
|
18
|
Molléro R, Pennec X, Delingette H, Ayache N, Sermesant M. Population-based priors in cardiac model personalisation for consistent parameter estimation in heterogeneous databases. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3158. [PMID: 30239175 DOI: 10.1002/cnm.3158] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 09/10/2018] [Accepted: 09/16/2018] [Indexed: 06/08/2023]
Abstract
Personalised cardiac models are a virtual representation of the patient heart, with parameter values for which the simulation fits the available clinical measurements. Models usually have a large number of parameters while the available data for a given patient are typically limited to a small set of measurements; thus, the parameters cannot be estimated uniquely. This is a practical obstacle for clinical applications, where accurate parameter values can be important. Here, we explore an original approach based on an algorithm called Iteratively Updated Priors (IUP), in which we perform successive personalisations of a full database through maximum a posteriori (MAP) estimation, where the prior probability at an iteration is set from the distribution of personalised parameters in the database at the previous iteration. At the convergence of the algorithm, estimated parameters of the population lie on a linear subspace of reduced (and possibly sufficient) dimension in which for each case of the database, there is a (possibly unique) parameter value for which the simulation fits the measurements. We first show how this property can help the modeller select a relevant parameter subspace for personalisation. In addition, since the resulting priors in this subspace represent the population statistics in this subspace, they can be used to perform consistent parameter estimation for cases where measurements are possibly different or missing in the database, which we illustrate with the personalisation of a heterogeneous database of 811 cases.
Collapse
Affiliation(s)
- Roch Molléro
- Inria, Epione Research Project, Sophia Antipolis, France
| | - Xavier Pennec
- Inria, Epione Research Project, Sophia Antipolis, France
| | | | | | | |
Collapse
|
19
|
Caruel M, Moireau P, Chapelle D. Stochastic modeling of chemical–mechanical coupling in striated muscles. Biomech Model Mechanobiol 2019; 18:563-587. [DOI: 10.1007/s10237-018-1102-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 11/21/2018] [Indexed: 01/15/2023]
|
20
|
Large Scale Cardiovascular Model Personalisation for Mechanistic Analysis of Heart and Brain Interactions. FUNCTIONAL IMAGING AND MODELING OF THE HEART 2019. [DOI: 10.1007/978-3-030-21949-9_31] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
|
21
|
Witzenburg CM, Holmes JW. Predicting the Time Course of Ventricular Dilation and Thickening Using a Rapid Compartmental Model. J Cardiovasc Transl Res 2018; 11:109-122. [PMID: 29550925 DOI: 10.1007/s12265-018-9793-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 02/05/2018] [Indexed: 12/11/2022]
Abstract
The ability to predict long-term growth and remodeling of the heart in individual patients could have important clinical implications, but the time to customize and run current models makes them impractical for routine clinical use. Therefore, we adapted a published growth relation for use in a compartmental model of the left ventricle (LV). The model was coupled to a circuit model of the circulation to simulate hemodynamic overload in dogs. We automatically tuned control and acute model parameters based on experimentally reported hemodynamic data and fit growth parameters to changes in LV dimensions from two experimental overload studies (one pressure, one volume). The fitted model successfully predicted the reported time course of LV dilation and thickening not only in independent studies of pressure and volume overload but also following myocardial infarction. Implemented in MATLAB on a desktop PC, the model required just 6 min to simulate 3 months of growth.
Collapse
Affiliation(s)
| | - Jeffrey W Holmes
- Biomedical Engineering, Medicine, and Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA.
| |
Collapse
|
22
|
Brault A, Dumas L, Lucor D. Uncertainty quantification of inflow boundary condition and proximal arterial stiffness-coupled effect on pulse wave propagation in a vascular network. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2859. [PMID: 27943622 DOI: 10.1002/cnm.2859] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 12/04/2016] [Indexed: 05/18/2023]
Affiliation(s)
- Antoine Brault
- Institut de Mathématiques de Toulouse; Université Paul Sabatier; 118 Route de Narbonne, 31062 Toulouse Cedex 4 France
| | - Laurent Dumas
- Laboratoire de Mathématiques de Versailles, UVSQ, CNRS; Université Paris-Saclay; Versailles 78035 France
| | - Didier Lucor
- LIMSI, CNRS; Université Paris-Saclay, Campus universitaire bât; 508 Rue John von Neumann Orsay Cedex F-91405 France
| |
Collapse
|
23
|
Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models. Biomech Model Mechanobiol 2017; 17:285-300. [PMID: 28894984 DOI: 10.1007/s10237-017-0960-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Accepted: 08/31/2017] [Indexed: 10/18/2022]
Abstract
Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified "0D" version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.
Collapse
|
24
|
FROLOV SV, SINDEEV SV, LISCHOUK VA, GAZIZOVA DSH, LIEPSCH D, BALASSO A. A LUMPED PARAMETER MODEL OF CARDIOVASCULAR SYSTEM WITH PULSATING HEART FOR DIAGNOSTIC STUDIES. J MECH MED BIOL 2016. [DOI: 10.1142/s0219519417500567] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Mathematical modeling of cardiovascular system provides an ability to study hemodynamics and to predict the results of treatment based on individual anatomical and physiological data of patients. However, the presently developed models of cardiovascular system have a limitation on use in clinical practice due to their physical and computational complexities. The aim of this study is to derive a lumped parameter model of cardiovascular system with pulsating heart in which all parameters have a physically based quantitative value and can be identified using clinical methods. For development of a cardiovascular system model the chamber analog was used which describes whole cardiovascular system as a set of elastic chambers. The proposed model consists of systemic and pulmonary circulation, four-chamber heart and four valves. The description of heart is based on a four-element representation of a cardiac muscle. The reverse blood flow via valves is considered. The accuracy of the derived model was evaluated by comparing the data of numerical simulation with experimental data. The limitations of the model were discussed as well as possible applications of the model were suggested. The proposed lumped parameter model can be used to support clinicians in their decisions in treating cardiovascular disorders.
Collapse
Affiliation(s)
- S. V. FROLOV
- Biomedical Engineering Department, Tambov State Technical University, Tambov, Sovetskaya Street, 106, Russia
| | - S. V. SINDEEV
- Biomedical Engineering Department, Tambov State Technical University, Tambov, Sovetskaya Street, 106, Russia
| | - V. A. LISCHOUK
- Laboratory of Mathematical Modeling and Monitoring, Bakoulev Center of Cardiovascular Surgery, Moscow, Roublyevskoe Shosse, 135, Russia
| | - D. SH. GAZIZOVA
- Laboratory of Mathematical Modeling and Monitoring, Bakoulev Center of Cardiovascular Surgery, Moscow, Roublyevskoe Shosse, 135, Russia
| | - D. LIEPSCH
- Department of Building Services Engineering, Paper and Packaging, Technology and Print and Media Technology, Munich University of Applied Sciences, Munich, Lothstrasse, 34, Germany
| | - A. BALASSO
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Ismaninger Strasse, 22, Germany
| |
Collapse
|
25
|
Chabiniok R, Wang VY, Hadjicharalambous M, Asner L, Lee J, Sermesant M, Kuhl E, Young AA, Moireau P, Nash MP, Chapelle D, Nordsletten DA. Multiphysics and multiscale modelling, data-model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics. Interface Focus 2016; 6:20150083. [PMID: 27051509 PMCID: PMC4759748 DOI: 10.1098/rsfs.2015.0083] [Citation(s) in RCA: 139] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
With heart and cardiovascular diseases continually challenging healthcare systems worldwide, translating basic research on cardiac (patho)physiology into clinical care is essential. Exacerbating this already extensive challenge is the complexity of the heart, relying on its hierarchical structure and function to maintain cardiovascular flow. Computational modelling has been proposed and actively pursued as a tool for accelerating research and translation. Allowing exploration of the relationships between physics, multiscale mechanisms and function, computational modelling provides a platform for improving our understanding of the heart. Further integration of experimental and clinical data through data assimilation and parameter estimation techniques is bringing computational models closer to use in routine clinical practice. This article reviews developments in computational cardiac modelling and how their integration with medical imaging data is providing new pathways for translational cardiac modelling.
Collapse
Affiliation(s)
- Radomir Chabiniok
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas’ Hospital, London SE1 7EH, UK
- Inria and Paris-Saclay University, Bâtiment Alan Turing, 1 rue Honoré d'Estienne d'Orves, Campus de l'Ecole Polytechnique, Palaiseau 91120, France
| | - Vicky Y. Wang
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Auckland, New Zealand
| | - Myrianthi Hadjicharalambous
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas’ Hospital, London SE1 7EH, UK
| | - Liya Asner
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas’ Hospital, London SE1 7EH, UK
| | - Jack Lee
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas’ Hospital, London SE1 7EH, UK
| | - Maxime Sermesant
- Inria, Asclepios team, 2004 route des Lucioles BP 93, Sophia Antipolis Cedex 06902, France
| | - Ellen Kuhl
- Departments of Mechanical Engineering, Bioengineering, and Cardiothoracic Surgery, Stanford University, 496 Lomita Mall, Durand 217, Stanford, CA 94306, USA
| | - Alistair A. Young
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Auckland, New Zealand
| | - Philippe Moireau
- Inria and Paris-Saclay University, Bâtiment Alan Turing, 1 rue Honoré d'Estienne d'Orves, Campus de l'Ecole Polytechnique, Palaiseau 91120, France
| | - Martyn P. Nash
- Auckland Bioengineering Institute, University of Auckland, 70 Symonds Street, Auckland, New Zealand
- Department of Engineering Science, University of Auckland, 70 Symonds Street, Auckland, New Zealand
| | - Dominique Chapelle
- Inria and Paris-Saclay University, Bâtiment Alan Turing, 1 rue Honoré d'Estienne d'Orves, Campus de l'Ecole Polytechnique, Palaiseau 91120, France
| | - David A. Nordsletten
- Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas’ Hospital, London SE1 7EH, UK
| |
Collapse
|
26
|
Pant S, Corsini C, Baker C, Hsia TY, Pennati G, Vignon-Clementel IE. Data assimilation and modelling of patient-specific single-ventricle physiology with and without valve regurgitation. J Biomech 2015; 49:2162-2173. [PMID: 26708918 DOI: 10.1016/j.jbiomech.2015.11.030] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2015] [Accepted: 11/11/2015] [Indexed: 10/22/2022]
Abstract
A closed-loop lumped parameter model of blood circulation is considered for single-ventricle shunt physiology. Its parameters are estimated by an inverse problem based on patient-specific haemodynamics measurements. As opposed to a black-box approach, maximizing the number of parameters that are related to physically measurable quantities motivates the present model. Heart chambers are described by a single-fibre mechanics model, and valve function is modelled with smooth opening and closure. A model for valve prolapse leading to valve regurgitation is proposed. The method of data assimilation, in particular the unscented Kalman filter, is used to estimate the model parameters from time-varying clinical measurements. This method takes into account both the uncertainty in prior knowledge related to the parameters and the uncertainty associated with the clinical measurements. Two patient-specific cases - one without regurgitation and one with atrioventricular valve regurgitation - are presented. Pulmonary and systemic circulation parameters are successfully estimated, without assumptions on their relationships. Parameters governing the behaviour of heart chambers and valves are either fixed based on biomechanics, or estimated. Results of the inverse problem are validated qualitatively through clinical measurements or clinical estimates that were not included in the parameter estimation procedure. The model and the estimation method are shown to successfully capture patient-specific clinical observations, even with regurgitation, such as the double peaked nature of valvular flows and anomalies in electrocardiogram readings. Lastly, biomechanical implications of the results are discussed.
Collapse
Affiliation(s)
- Sanjay Pant
- Inria Paris-Rocquencourt & Sorbonne Universités UPMC Paris 6, Laboratoire Jacques-Louis Lions, France.
| | - Chiara Corsini
- Laboratory of Biological Structure Mechanics, Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Italy
| | - Catriona Baker
- Cardiac Unit, UCL Institute of Cardiovascular Science, and Great Ormond Street Hospital for Children, London, UK
| | - Tain-Yen Hsia
- Cardiac Unit, UCL Institute of Cardiovascular Science, and Great Ormond Street Hospital for Children, London, UK
| | - Giancarlo Pennati
- Laboratory of Biological Structure Mechanics, Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta', Politecnico di Milano, Italy
| | - Irene E Vignon-Clementel
- Inria Paris-Rocquencourt & Sorbonne Universités UPMC Paris 6, Laboratoire Jacques-Louis Lions, France.
| | | |
Collapse
|
27
|
Hadjicharalambous M, Chabiniok R, Asner L, Sammut E, Wong J, Carr-White G, Lee J, Razavi R, Smith N, Nordsletten D. Analysis of passive cardiac constitutive laws for parameter estimation using 3D tagged MRI. Biomech Model Mechanobiol 2014; 14:807-28. [PMID: 25510227 PMCID: PMC4490188 DOI: 10.1007/s10237-014-0638-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2014] [Accepted: 11/28/2014] [Indexed: 01/25/2023]
Abstract
An unresolved issue in patient-specific models of cardiac mechanics is the choice of an appropriate constitutive law, able to accurately capture the passive behavior of the myocardium, while still having uniquely identifiable parameters tunable from available clinical data. In this paper, we aim to facilitate this choice by examining the practical identifiability and model fidelity of constitutive laws often used in cardiac mechanics. Our analysis focuses on the use of novel 3D tagged MRI, providing detailed displacement information in three dimensions. The practical identifiability of each law is examined by generating synthetic 3D tags from in silico simulations, allowing mapping of the objective function landscape over parameter space and comparison of minimizing parameter values with original ground truth values. Model fidelity was tested by comparing these laws with the more complex transversely isotropic Guccione law, by characterizing their passive end-diastolic pressure–volume relation behavior, as well as by considering the in vivo case of a healthy volunteer. These results show that a reduced form of the Holzapfel–Ogden law provides the best balance between identifiability and model fidelity across the tests considered.
Collapse
Affiliation(s)
- Myrianthi Hadjicharalambous
- Division of Imaging Sciences and Biomedical Engineering, King's College London, 4th Floor, Lambeth Wing St. Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK,
| | | | | | | | | | | | | | | | | | | |
Collapse
|