1
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Validating MRI-Derived Myocardial Stiffness Estimates Using In Vitro Synthetic Heart Models. Ann Biomed Eng 2023:10.1007/s10439-023-03164-7. [PMID: 36914919 DOI: 10.1007/s10439-023-03164-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 02/07/2023] [Indexed: 03/16/2023]
Abstract
Impaired cardiac filling in response to increased passive myocardial stiffness contributes to the pathophysiology of heart failure. By leveraging cardiac MRI data and ventricular pressure measurements, we can estimate in vivo passive myocardial stiffness using personalized inverse finite element models. While it is well-known that this approach is subject to uncertainties, only few studies quantify the accuracy of these stiffness estimates. This lack of validation is, at least in part, due to the absence of ground truth in vivo passive myocardial stiffness values. Here, using 3D printing, we created soft, homogenous, isotropic, hyperelastic heart phantoms of varying geometry and stiffness and simulate diastolic filling by incorporating the phantoms into an MRI-compatible left ventricular inflation system. We estimate phantom stiffness from MRI and pressure data using inverse finite element analyses based on a Neo-Hookean model. We demonstrate that our identified softest and stiffest values of 215.7 and 512.3 kPa agree well with the ground truth of 226.2 and 526.4 kPa. Overall, our estimated stiffnesses revealed a good agreement with the ground truth ([Formula: see text] error) across all models. Our results suggest that MRI-driven computational constitutive modeling can accurately estimate synthetic heart material stiffnesses in the range of 200-500 kPa.
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2
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Marx L, Niestrawska JA, Gsell MA, Caforio F, Plank G, Augustin CM. Robust and efficient fixed-point algorithm for the inverse elastostatic problem to identify myocardial passive material parameters and the unloaded reference configuration. JOURNAL OF COMPUTATIONAL PHYSICS 2022; 463:111266. [PMID: 35662800 PMCID: PMC7612790 DOI: 10.1016/j.jcp.2022.111266] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Image-based computational models of the heart represent a powerful tool to shed new light on the mechanisms underlying physiological and pathological conditions in cardiac function and to improve diagnosis and therapy planning. However, in order to enable the clinical translation of such models, it is crucial to develop personalized models that are able to reproduce the physiological reality of a given patient. There have been numerous contributions in experimental and computational biomechanics to characterize the passive behavior of the myocardium. However, most of these studies suffer from severe limitations and are not applicable to high-resolution geometries. In this work, we present a novel methodology to perform an automated identification of in vivo properties of passive cardiac biomechanics. The highly-efficient algorithm fits material parameters against the shape of a patient-specific approximation of the end-diastolic pressure-volume relation (EDPVR). Simultaneously, an unloaded reference configuration is generated, where a novel line search strategy to improve convergence and robustness is implemented. Only clinical image data or previously generated meshes at one time point during diastole and one measured data point of the EDPVR are required as an input. The proposed method can be straightforwardly coupled to existing finite element (FE) software packages and is applicable to different constitutive laws and FE formulations. Sensitivity analysis demonstrates that the algorithm is robust with respect to initial input parameters.
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Affiliation(s)
- Laura Marx
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Justyna A. Niestrawska
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A.F. Gsell
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Federica Caforio
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
- Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Christoph M. Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Corresponding author at: Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Neue Stiftingtalstrasse 6/D04, 8010 Graz, Austria. (C.M.Augustin)
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3
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Miller R, Kerfoot E, Mauger C, Ismail TF, Young AA, Nordsletten DA. An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline. Front Physiol 2021; 12:716597. [PMID: 34603077 PMCID: PMC8481785 DOI: 10.3389/fphys.2021.716597] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/06/2021] [Indexed: 02/04/2023] Open
Abstract
Parameterised patient-specific models of the heart enable quantitative analysis of cardiac function as well as estimation of regional stress and intrinsic tissue stiffness. However, the development of personalised models and subsequent simulations have often required lengthy manual setup, from image labelling through to generating the finite element model and assigning boundary conditions. Recently, rapid patient-specific finite element modelling has been made possible through the use of machine learning techniques. In this paper, utilising multiple neural networks for image labelling and detection of valve landmarks, together with streamlined data integration, a pipeline for generating patient-specific biventricular models is applied to clinically-acquired data from a diverse cohort of individuals, including hypertrophic and dilated cardiomyopathy patients and healthy volunteers. Valve motion from tracked landmarks as well as cavity volumes measured from labelled images are used to drive realistic motion and estimate passive tissue stiffness values. The neural networks are shown to accurately label cardiac regions and features for these diverse morphologies. Furthermore, differences in global intrinsic parameters, such as tissue anisotropy and normalised active tension, between groups illustrate respective underlying changes in tissue composition and/or structure as a result of pathology. This study shows the successful application of a generic pipeline for biventricular modelling, incorporating artificial intelligence solutions, within a diverse cohort.
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Affiliation(s)
- Renee Miller
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Charlène Mauger
- Auckland MR Research Group, University of Auckland, Auckland, New Zealand
| | - Tevfik F. Ismail
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Alistair A. Young
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Auckland MR Research Group, University of Auckland, Auckland, New Zealand
| | - David A. Nordsletten
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering and Cardiac Surgery, University of Michigan, Ann Arbor, MI, United States
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4
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Kallhovd S, Sundnes J, Wall ST. Sensitivity of stress and strain calculations to passive material parameters in cardiac mechanical models using unloaded geometries. Comput Methods Biomech Biomed Engin 2019; 22:664-675. [DOI: 10.1080/10255842.2019.1579312] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
| | - J. Sundnes
- Simula Research Laboratory, Lysaker, Norway
| | - S. T. Wall
- Simula Research Laboratory, Lysaker, Norway
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5
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Parker MD, Babarenda Gamage TP, HajiRassouliha A, Taberner AJ, Nash MP, Nielsen PMF. Surface deformation tracking and modelling of soft materials. Biomech Model Mechanobiol 2019; 18:1031-1045. [PMID: 30778884 DOI: 10.1007/s10237-019-01127-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 02/09/2019] [Indexed: 11/27/2022]
Abstract
Many computer vision algorithms have been presented to track surface deformations, but few have provided a direct comparison of measurements with other stereoscopic approaches and physics-based models. We have previously developed a phase-based cross-correlation algorithm to track dense distributions of displacements over three-dimensional surfaces. In the present work, we compare this algorithm with one that uses an independent tracking system, derived from an array of fluorescent microspheres. A smooth bicubic Hermite mesh was fitted to deformations obtained from the phase-based cross-correlation data. This mesh was then used to estimate the microsphere locations, which were compared to stereo reconstructions of the microsphere positions. The method was applied to a 35 mm × 35 mm × 35 mm soft silicone gel cube under indentation, with three square bands of microspheres placed around the indenter tip. At an indentation depth of 4.5 mm, the root-mean-square (RMS) differences between the reconstructed positions of the microspheres and their identified positions for the inner, middle, and outer bands were 60 µm, 20 µm, and 19 µm, respectively. The usefulness of the strain-tracking data for physics-based finite element modelling of large deformation mechanics was then demonstrated by estimating a neo-Hookean stiffness parameter for the gel. At the optimal constitutive parameter estimate, the RMS difference between the measured microsphere positions and their finite element model-predicted locations was 143 µm.
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Affiliation(s)
- Matthew D Parker
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | | | - Amir HajiRassouliha
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Andrew J Taberner
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Engineering Science, University of Auckland, Auckland, New Zealand
| | - Poul M F Nielsen
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
- Department of Engineering Science, University of Auckland, Auckland, New Zealand.
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6
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Khan S, Fakhouri F, Majeed W, Kolipaka A. Cardiovascular magnetic resonance elastography: A review. NMR IN BIOMEDICINE 2018; 31:e3853. [PMID: 29193358 PMCID: PMC5975119 DOI: 10.1002/nbm.3853] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 08/25/2017] [Accepted: 09/29/2017] [Indexed: 05/19/2023]
Abstract
Cardiovascular diseases are the leading cause of death worldwide. These cardiovascular diseases are associated with mechanical changes in the myocardium and aorta. It is known that stiffness is altered in many diseases, including the spectrum of ischemia, diastolic dysfunction, hypertension and hypertrophic cardiomyopathy. In addition, the stiffness of the aortic wall is altered in multiple diseases, including hypertension, coronary artery disease and aortic aneurysm formation. For example, in diastolic dysfunction in which the ejection fraction is preserved, stiffness can potentially be an important biomarker. Similarly, in aortic aneurysms, stiffness can provide valuable information with regard to rupture potential. A number of studies have addressed invasive and non-invasive approaches to test and measure the mechanical properties of the myocardium and aorta. One of the non-invasive approaches is magnetic resonance elastography (MRE). MRE is a phase-contrast magnetic resonance imaging technique that measures tissue stiffness non-invasively. This review article highlights the technical details and application of MRE in the quantification of myocardial and aortic stiffness in different disease states.
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Affiliation(s)
- Saad Khan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Faisal Fakhouri
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, 43210, USA
| | - Waqas Majeed
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Arunark Kolipaka
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, 43210, USA
- Department of Internal Medicine-Division of Cardiology, The Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
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7
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Wang ZJ, Wang VY, Bradley CP, Nash MP, Young AA, Cao JJ. Left Ventricular Diastolic Myocardial Stiffness and End-Diastolic Myofibre Stress in Human Heart Failure Using Personalised Biomechanical Analysis. J Cardiovasc Transl Res 2018; 11:346-356. [PMID: 29998358 DOI: 10.1007/s12265-018-9816-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Accepted: 06/26/2018] [Indexed: 01/08/2023]
Abstract
Understanding the aetiology of heart failure with preserved (HFpEF) and reduced (HFrEF) ejection fraction requires knowledge of biomechanical factors such as diastolic myocardial stiffness and stress. Cine CMR images and intra-ventricular pressure recordings were acquired in 8 HFrEF, 11 HFpEF and 5 control subjects. Diastolic myocardial stiffness was estimated using biomechanical models and found to be greater in HFrEF (6.4 ± 1.2 kPa) than HFpEF (2.7 ± 0.6 kPa, p < 0.05) and also greater than control (1.2 ± 0.4 kPa, p < 0.005). End-diastolic mid-ventricular myofibre stress derived from the personalised biomechanics model was higher in HFrEF (2.9 ± 0.3 kPa) than control (0.9 ± 0.3 kPa, p < 0.01). Chamber stiffness, measured from the slope of the diastolic pressure-volume relationship, is determined by the intrinsic tissue properties as well as the size and shape of the heart, and was unable to distinguish between any of the three groups (p > 0.05). Personalised biomechanical analysis may provide more specific information about myocardial mechanical behaviour than global chamber indices, which are confounded by variations in ventricular geometry.
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Affiliation(s)
- Zhinuo J Wang
- Auckland Bioengineering Institute, University of Auckland, Level 6 Reception, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Vicky Y Wang
- Auckland Bioengineering Institute, University of Auckland, Level 6 Reception, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Chris P Bradley
- Auckland Bioengineering Institute, University of Auckland, Level 6 Reception, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand
| | - Martyn P Nash
- Auckland Bioengineering Institute, University of Auckland, Level 6 Reception, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand. .,Department of Engineering Science, University of Auckland, Auckland, New Zealand.
| | - Alistair A Young
- Auckland Bioengineering Institute, University of Auckland, Level 6 Reception, 70 Symonds Street, Grafton, Auckland, 1010, New Zealand.,Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
| | - J Jane Cao
- The Heart Center, St Francis Hospital, Roslyn, NY, USA
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8
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Balaban G, Finsberg H, Odland HH, Rognes ME, Ross S, Sundnes J, Wall S. High-resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33:e2863. [PMID: 28039961 DOI: 10.1002/cnm.2863] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Revised: 10/31/2016] [Accepted: 12/28/2016] [Indexed: 06/06/2023]
Abstract
Computational models of cardiac mechanics, personalized to a patient, offer access to mechanical information above and beyond direct medical imaging. Additionally, such models can be used to optimize and plan therapies in-silico, thereby reducing risks and improving patient outcome. Model personalization has traditionally been achieved by data assimilation, which is the tuning or optimization of model parameters to match patient observations. Current data assimilation procedures for cardiac mechanics are limited in their ability to efficiently handle high-dimensional parameters. This restricts parameter spatial resolution, and thereby the ability of a personalized model to account for heterogeneities that are often present in a diseased or injured heart. In this paper, we address this limitation by proposing an adjoint gradient-based data assimilation method that can efficiently handle high-dimensional parameters. We test this procedure on a synthetic data set and provide a clinical example with a dyssynchronous left ventricle with highly irregular motion. Our results show that the method efficiently handles a high-dimensional optimization parameter and produces an excellent agreement for personalized models to both synthetic and clinical data.
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Affiliation(s)
- Gabriel Balaban
- Simula Research Laboratory, P.O. Box 134 1325 Lysaker, Norway
- Department of Informatics, University of Oslo, P.O. Box 1080, Blindern 0316 Oslo, Norway
- Center for Cardiological Innovation, Songsvannsveien 9, 0372 Oslo, Norway
| | - Henrik Finsberg
- Simula Research Laboratory, P.O. Box 134 1325 Lysaker, Norway
- Department of Informatics, University of Oslo, P.O. Box 1080, Blindern 0316 Oslo, Norway
- Center for Cardiological Innovation, Songsvannsveien 9, 0372 Oslo, Norway
| | - Hans Henrik Odland
- Faculty of Medicine, University of Oslo, P.O. Box 1078 Blindern, 0316 Oslo, Norway
- Department of Pediatrics, Oslo University Hospital, PO Nydalen, Oslo, Norway
| | - Marie E Rognes
- Simula Research Laboratory, P.O. Box 134 1325 Lysaker, Norway
- Department of Mathematics, University of Oslo, P.O Box 1053, Blindern 0316 Oslo, Norway
| | - Stian Ross
- Faculty of Medicine, University of Oslo, P.O. Box 1078 Blindern, 0316 Oslo, Norway
- Center for Cardiological Innovation, Songsvannsveien 9, 0372 Oslo, Norway
| | - Joakim Sundnes
- Simula Research Laboratory, P.O. Box 134 1325 Lysaker, Norway
- Department of Informatics, University of Oslo, P.O. Box 1080, Blindern 0316 Oslo, Norway
- Center for Cardiological Innovation, Songsvannsveien 9, 0372 Oslo, Norway
| | - Samuel Wall
- Simula Research Laboratory, P.O. Box 134 1325 Lysaker, Norway
- Center for Cardiological Innovation, Songsvannsveien 9, 0372 Oslo, Norway
- Department of Mathematical Science and Technology, Norwegian University of Life Sciences, Universitetstunet 3 1430 Ås, Norway
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9
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Leung S, McGlashan SR, Musson DSP, Cornish J, Anderson IA, Shim VBK. Investigations of Strain Fields in 3D Hydrogels Under Dynamic Confined Loading. J Med Biol Eng 2017. [DOI: 10.1007/s40846-017-0319-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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10
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Behdadfar S, Navarro L, Sundnes J, Maleckar MM, Avril S. Importance of material parameters and strain energy function on the wall stresses in the left ventricle. Comput Methods Biomech Biomed Engin 2017; 20:1223-1232. [DOI: 10.1080/10255842.2017.1347160] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Sareh Behdadfar
- Ecole Nationale Supérieure des Mines, CIS-EMSE, Institut national de la santé et de la recherche médicale, INSERM:UMR1059, SAINBIOSE, Saint-Etienne, France
| | - Laurent Navarro
- Ecole Nationale Supérieure des Mines, CIS-EMSE, Institut national de la santé et de la recherche médicale, INSERM:UMR1059, SAINBIOSE, Saint-Etienne, France
| | - Joakim Sundnes
- Computational Cardiac Modeling Department, Simula Research Laboratory, Fornebu, Norway
| | - Molly M. Maleckar
- Computational Cardiac Modeling Department, Simula Research Laboratory, Fornebu, Norway
- Modeling at the Allen Institute for Cell Science, Allen Institute, Seattle, WA, USA
| | - Stéphane Avril
- Ecole Nationale Supérieure des Mines, CIS-EMSE, Institut national de la santé et de la recherche médicale, INSERM:UMR1059, SAINBIOSE, Saint-Etienne, France
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11
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Nasopoulou A, Shetty A, Lee J, Nordsletten D, Rinaldi CA, Lamata P, Niederer S. Improved identifiability of myocardial material parameters by an energy-based cost function. Biomech Model Mechanobiol 2017. [PMID: 28188386 DOI: 10.1007/s10237‐016‐0865‐3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometry-based CF is unable to uniquely constrain the parameter space. The energy-based CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were [Formula: see text] and [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]) for the HF cases and [Formula: see text] and [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]) for the healthy case.
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Affiliation(s)
- Anastasia Nasopoulou
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Anoop Shetty
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Jack Lee
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - David Nordsletten
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - C Aldo Rinaldi
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
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12
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Nasopoulou A, Shetty A, Lee J, Nordsletten D, Rinaldi CA, Lamata P, Niederer S. Improved identifiability of myocardial material parameters by an energy-based cost function. Biomech Model Mechanobiol 2017; 16:971-988. [PMID: 28188386 PMCID: PMC5480093 DOI: 10.1007/s10237-016-0865-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Accepted: 12/09/2016] [Indexed: 12/16/2022]
Abstract
Myocardial stiffness is a valuable clinical biomarker for the monitoring and stratification of heart failure (HF). Cardiac finite element models provide a biomechanical framework for the assessment of stiffness through the determination of the myocardial constitutive model parameters. The reported parameter intercorrelations in popular constitutive relations, however, obstruct the unique estimation of material parameters and limit the reliable translation of this stiffness metric to clinical practice. Focusing on the role of the cost function (CF) in parameter identifiability, we investigate the performance of a set of geometric indices (based on displacements, strains, cavity volume, wall thickness and apicobasal dimension of the ventricle) and a novel CF derived from energy conservation. Our results, with a commonly used transversely isotropic material model (proposed by Guccione et al.), demonstrate that a single geometry-based CF is unable to uniquely constrain the parameter space. The energy-based CF, conversely, isolates one of the parameters and in conjunction with one of the geometric metrics provides a unique estimation of the parameter set. This gives rise to a new methodology for estimating myocardial material parameters based on the combination of deformation and energetics analysis. The accuracy of the pipeline is demonstrated in silico, and its robustness in vivo, in a total of 8 clinical data sets (7 HF and one control). The mean identified parameters of the Guccione material law were [Formula: see text] and [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]) for the HF cases and [Formula: see text] and [Formula: see text] ([Formula: see text], [Formula: see text], [Formula: see text]) for the healthy case.
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Affiliation(s)
- Anastasia Nasopoulou
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Anoop Shetty
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Jack Lee
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - David Nordsletten
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - C Aldo Rinaldi
- Cardiovascular Department, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
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13
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Asner L, Hadjicharalambous M, Chabiniok R, Peresutti D, Sammut E, Wong J, Carr-White G, Chowienczyk P, Lee J, King A, Smith N, Razavi R, Nordsletten D. Estimation of passive and active properties in the human heart using 3D tagged MRI. Biomech Model Mechanobiol 2016; 15:1121-39. [PMID: 26611908 PMCID: PMC5021775 DOI: 10.1007/s10237-015-0748-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 11/09/2015] [Indexed: 11/21/2022]
Abstract
Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and-through patient-specific modelling-derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechanical analysis.
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Affiliation(s)
- Liya Asner
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK.
| | - Myrianthi Hadjicharalambous
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Radomir Chabiniok
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
- Inria Saclay Ile-de-France, MΞDISIM Team, Palaiseau, France
| | - Devis Peresutti
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Eva Sammut
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - James Wong
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Gerald Carr-White
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
- Department of Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, SE1 7EH, UK
| | - Philip Chowienczyk
- Department of Clinical Pharmacology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Jack Lee
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Andrew King
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Nicolas Smith
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
- Faculty of Engineering, University of Auckland, Auckland, New Zealand
| | - Reza Razavi
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - David Nordsletten
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
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14
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Neumann D, Mansi T, Itu L, Georgescu B, Kayvanpour E, Sedaghat-Hamedani F, Amr A, Haas J, Katus H, Meder B, Steidl S, Hornegger J, Comaniciu D. A self-taught artificial agent for multi-physics computational model personalization. Med Image Anal 2016; 34:52-64. [PMID: 27133269 DOI: 10.1016/j.media.2016.04.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 04/08/2016] [Accepted: 04/19/2016] [Indexed: 02/05/2023]
Abstract
Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.
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Affiliation(s)
- Dominik Neumann
- Medical Imaging Technologies, Siemens Healthcare GmbH, Erlangen, Germany; Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany.
| | - Tommaso Mansi
- Medical Imaging Technologies, Siemens Healthcare, Princeton, USA
| | - Lucian Itu
- Siemens Corporate Technology, Siemens SRL, Brasov, Romania; Transilvania University of Brasov, Brasov, Romania
| | - Bogdan Georgescu
- Medical Imaging Technologies, Siemens Healthcare, Princeton, USA
| | - Elham Kayvanpour
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | | | - Ali Amr
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | - Jan Haas
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | - Hugo Katus
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | - Benjamin Meder
- Department of Internal Medicine III, University Hospital Heidelberg, Germany
| | - Stefan Steidl
- Pattern Recognition Lab, FAU Erlangen-Nürnberg, Erlangen, Germany
| | | | - Dorin Comaniciu
- Medical Imaging Technologies, Siemens Healthcare, Princeton, USA
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15
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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.
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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
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16
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Adjoint multi-start-based estimation of cardiac hyperelastic material parameters using shear data. Biomech Model Mechanobiol 2016; 15:1509-1521. [PMID: 27008196 PMCID: PMC5106512 DOI: 10.1007/s10237-016-0780-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Accepted: 03/03/2016] [Indexed: 11/27/2022]
Abstract
Cardiac muscle tissue during relaxation is commonly modeled as a hyperelastic material with strongly nonlinear and anisotropic stress response. Adapting the behavior of such a model to experimental or patient data gives rise to a parameter estimation problem which involves a significant number of parameters. Gradient-based optimization algorithms provide a way to solve such nonlinear parameter estimation problems with relatively few iterations, but require the gradient of the objective functional with respect to the model parameters. This gradient has traditionally been obtained using finite differences, the calculation of which scales linearly with the number of model parameters, and introduces a differencing error. By using an automatically derived adjoint equation, we are able to calculate this gradient more efficiently, and with minimal implementation effort. We test this adjoint framework on a least squares fitting problem involving data from simple shear tests on cardiac tissue samples. A second challenge which arises in gradient-based optimization is the dependency of the algorithm on a suitable initial guess. We show how a multi-start procedure can alleviate this dependency. Finally, we provide estimates for the material parameters of the Holzapfel and Ogden strain energy law using finite element models together with experimental shear data.
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17
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Affiliation(s)
- V.Y. Wang
- Auckland Bioengineering Institute and
| | - P.M.F. Nielsen
- Auckland Bioengineering Institute and
- Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland 1010, New Zealand; , ,
| | - M.P. Nash
- Auckland Bioengineering Institute and
- Department of Engineering Science, Faculty of Engineering, University of Auckland, Auckland 1010, New Zealand; , ,
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18
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Asner L, Hadjicharalambous M, Chabiniok R, Peresutti D, Sammut E, Wong J, Carr-White G, Chowienczyk P, Lee J, King A, Smith N, Razavi R, Nordsletten D. Estimation of passive and active properties in the human heart using 3D tagged MRI. Biomech Model Mechanobiol 2015. [PMID: 26611908 DOI: 10.1007/s10237‐015‐0748‐z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
Abstract
Advances in medical imaging and image processing are paving the way for personalised cardiac biomechanical modelling. Models provide the capacity to relate kinematics to dynamics and-through patient-specific modelling-derived material parameters to underlying cardiac muscle pathologies. However, for clinical utility to be achieved, model-based analyses mandate robust model selection and parameterisation. In this paper, we introduce a patient-specific biomechanical model for the left ventricle aiming to balance model fidelity with parameter identifiability. Using non-invasive data and common clinical surrogates, we illustrate unique identifiability of passive and active parameters over the full cardiac cycle. Identifiability and accuracy of the estimates in the presence of controlled noise are verified with a number of in silico datasets. Unique parametrisation is then obtained for three datasets acquired in vivo. The model predictions show good agreement with the data extracted from the images providing a pipeline for personalised biomechanical analysis.
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Affiliation(s)
- Liya Asner
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK.
| | - Myrianthi Hadjicharalambous
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Radomir Chabiniok
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK.,Inria Saclay Ile-de-France, MΞDISIM Team, Palaiseau, France
| | - Devis Peresutti
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Eva Sammut
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - James Wong
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Gerald Carr-White
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK.,Department of Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, SE1 7EH, UK
| | - Philip Chowienczyk
- Department of Clinical Pharmacology, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Jack Lee
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Andrew King
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - Nicolas Smith
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK.,Faculty of Engineering, University of Auckland, Auckland, New Zealand
| | - Reza Razavi
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
| | - David Nordsletten
- Division of Imaging Sciences and Biomedical Engineering, St Thomas' Hospital, King's College London, 4th Floor, Lambeth Wing, London, SE1 7EH, UK
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19
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Clinical Diagnostic Biomarkers from the Personalization of Computational Models of Cardiac Physiology. Ann Biomed Eng 2015; 44:46-57. [PMID: 26399986 DOI: 10.1007/s10439-015-1439-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 08/25/2015] [Indexed: 10/23/2022]
Abstract
Computational modelling of the heart is rapidly advancing to the point of clinical utility. However, the difficulty of parameterizing and validating models from clinical data indicates that the routine application of truly predictive models remains a significant challenge. We argue there is significant value in an intermediate step towards prediction. This step is the use of biophysically based models to extract clinically useful information from existing patient data. Specifically in this paper we review methodologies for applying modelling frameworks for this goal in the areas of quantifying cardiac anatomy, estimating myocardial stiffness and optimizing measurements of coronary perfusion. Using these indicative examples of the general overarching approach, we finally discuss the value, ongoing challenges and future potential for applying biophysically based modelling in the clinical context.
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20
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Myocardial Stiffness Estimation: A Novel Cost Function for Unique Parameter Identification. ACTA ACUST UNITED AC 2015. [DOI: 10.1007/978-3-319-20309-6_41] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
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21
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Bhatnagar T, Liu J, Yung A, Cripton P, Kozlowski P, Tetzlaff W, Oxland T. Quantifying the internal deformation of the rodent spinal cord during acute spinal cord injury – the validation of a method. Comput Methods Biomech Biomed Engin 2015; 19:386-95. [DOI: 10.1080/10255842.2015.1032944] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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22
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Gao H, Li WG, Cai L, Berry C, Luo XY. Parameter estimation in a Holzapfel-Ogden law for healthy myocardium. JOURNAL OF ENGINEERING MATHEMATICS 2015; 95:231-248. [PMID: 26663931 PMCID: PMC4662962 DOI: 10.1007/s10665-014-9740-3] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Accepted: 08/10/2014] [Indexed: 05/26/2023]
Abstract
A central problem in biomechanical studies of personalized human left ventricular (LV) modelling is to estimate material properties from in vivo clinical measurements. In this work we evaluate the passive myocardial mechanical properties inversely from the in vivo LV chamber pressure-volume and strain data. The LV myocardium is described using a structure-based orthotropic Holzapfel-Ogden constitutive law with eight parameters. In the first part of the paper we demonstrate how to use a multi-step non-linear least-squares optimization procedure to inversely estimate the parameters from the pressure-volume and strain data obtained from a synthetic LV model in diastole. In the second part, we show that to apply this procedure to clinical situations with limited in vivo data, additional constraints are required in the optimization procedure. Our study, based on three different healthy volunteers, demonstrates that the parameters of the Holzapfel-Ogden law could be extracted from pressure-volume and strain data with a suitable multi-step optimization procedure. Although the uniqueness of the solution cannot be addressed using our approaches, the material response is shown to be robustly determined.
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Affiliation(s)
- H. Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - W. G. Li
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - L. Cai
- School of Science, Northwestern Polytechnical University Xi’an, Xi’an, 710072 Shaanxi People’s Republic of China
| | - C. Berry
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - X. Y. Luo
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
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23
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STACOM Challenge: Simulating Left Ventricular Mechanics in the Canine Heart. LECTURE NOTES IN COMPUTER SCIENCE 2015. [DOI: 10.1007/978-3-319-14678-2_13] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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24
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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.
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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,
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25
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Estimating passive mechanical properties in a myocardial infarction using MRI and finite element simulations. Biomech Model Mechanobiol 2014. [PMID: 25315521 DOI: 10.1007/s10237‐014‐0627‐z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
Abstract
Myocardial infarction (MI) triggers a series of maladaptive events that lead to structural and functional changes in the left ventricle. It is crucial to better understand the progression of adverse remodeling, in order to develop effective treatment. In addition, being able to assess changes in vivo would be a powerful tool in the clinic. The goal of the current study is to quantify the in vivo material properties of infarcted and remote myocardium 1 week after MI, as well as the orientation of collagen fibers in the infarct. This will be accomplished by using a combination of magnetic resonance imaging (MRI), catheterization, finite element modeling, and numerical optimization to analyze a porcine model ([Formula: see text]) of posterolateral myocardial infarction. Specifically, properties will be determined by minimizing the difference between in vivo strains and volume calculated from MRI and finite element model predicted strains and volume. The results indicate that the infarct region is stiffer than the remote region and that the infarct collagen fibers become more circumferentially oriented 1 week post-MI. These findings are consistent with previous studies, which employed ex vivo techniques. The proposed methodology will ultimately provide a means of predicting remote and infarct mechanical properties in vivo at any time point post-MI.
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26
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Estimating passive mechanical properties in a myocardial infarction using MRI and finite element simulations. Biomech Model Mechanobiol 2014; 14:633-47. [PMID: 25315521 DOI: 10.1007/s10237-014-0627-z] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Accepted: 10/06/2014] [Indexed: 10/24/2022]
Abstract
Myocardial infarction (MI) triggers a series of maladaptive events that lead to structural and functional changes in the left ventricle. It is crucial to better understand the progression of adverse remodeling, in order to develop effective treatment. In addition, being able to assess changes in vivo would be a powerful tool in the clinic. The goal of the current study is to quantify the in vivo material properties of infarcted and remote myocardium 1 week after MI, as well as the orientation of collagen fibers in the infarct. This will be accomplished by using a combination of magnetic resonance imaging (MRI), catheterization, finite element modeling, and numerical optimization to analyze a porcine model ([Formula: see text]) of posterolateral myocardial infarction. Specifically, properties will be determined by minimizing the difference between in vivo strains and volume calculated from MRI and finite element model predicted strains and volume. The results indicate that the infarct region is stiffer than the remote region and that the infarct collagen fibers become more circumferentially oriented 1 week post-MI. These findings are consistent with previous studies, which employed ex vivo techniques. The proposed methodology will ultimately provide a means of predicting remote and infarct mechanical properties in vivo at any time point post-MI.
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27
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Knutsen AK, Magrath E, McEntee JE, Xing F, Prince JL, Bayly PV, Butman JA, Pham DL. Improved measurement of brain deformation during mild head acceleration using a novel tagged MRI sequence. J Biomech 2014; 47:3475-81. [PMID: 25287113 DOI: 10.1016/j.jbiomech.2014.09.010] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2014] [Revised: 09/06/2014] [Accepted: 09/14/2014] [Indexed: 02/06/2023]
Abstract
In vivo measurements of human brain deformation during mild acceleration are needed to help validate computational models of traumatic brain injury and to understand the factors that govern the mechanical response of the brain. Tagged magnetic resonance imaging is a powerful, noninvasive technique to track tissue motion in vivo which has been used to quantify brain deformation in live human subjects. However, these prior studies required from 72 to 144 head rotations to generate deformation data for a single image slice, precluding its use to investigate the entire brain in a single subject. Here, a novel method is introduced that significantly reduces temporal variability in the acquisition and improves the accuracy of displacement estimates. Optimization of the acquisition parameters in a gelatin phantom and three human subjects leads to a reduction in the number of rotations from 72 to 144 to as few as 8 for a single image slice. The ability to estimate accurate, well-resolved, fields of displacement and strain in far fewer repetitions will enable comprehensive studies of acceleration-induced deformation throughout the human brain in vivo.
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Affiliation(s)
- Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD, USA.
| | - Elizabeth Magrath
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD, USA
| | - Julie E McEntee
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD, USA
| | - Fangxu Xing
- Department of Electrical and Computing Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jerry L Prince
- Department of Electrical and Computing Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Philip V Bayly
- Department of Mechanical Engineering and Materials Science, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University, St. Louis, MO, USA
| | - John A Butman
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD, USA; Radiology and Imaging Sciences, Department of Diagnostic Radiology, Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation, Bethesda, MD, USA
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28
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Nordbø O, Lamata P, Land S, Niederer S, Aronsen JM, Louch WE, Sjaastad I, Martens H, Gjuvsland AB, Tøndel K, Torp H, Lohezic M, Schneider JE, Remme EW, Smith N, Omholt SW, Vik JO. A computational pipeline for quantification of mouse myocardial stiffness parameters. Comput Biol Med 2014; 53:65-75. [PMID: 25129018 DOI: 10.1016/j.compbiomed.2014.07.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Revised: 07/04/2014] [Accepted: 07/20/2014] [Indexed: 10/24/2022]
Abstract
The mouse is an important model for theoretical-experimental cardiac research, and biophysically based whole organ models of the mouse heart are now within reach. However, the passive material properties of mouse myocardium have not been much studied. We present an experimental setup and associated computational pipeline to quantify these stiffness properties. A mouse heart was excised and the left ventricle experimentally inflated from 0 to 1.44kPa in eleven steps, and the resulting deformation was estimated by echocardiography and speckle tracking. An in silico counterpart to this experiment was built using finite element methods and data on ventricular tissue microstructure from diffusion tensor MRI. This model assumed a hyperelastic, transversely isotropic material law to describe the force-deformation relationship, and was simulated for many parameter scenarios, covering the relevant range of parameter space. To identify well-fitting parameter scenarios, we compared experimental and simulated outcomes across the whole range of pressures, based partly on gross phenotypes (volume, elastic energy, and short- and long-axis diameter), and partly on node positions in the geometrical mesh. This identified a narrow region of experimentally compatible values of the material parameters. Estimation turned out to be more precise when based on changes in gross phenotypes, compared to the prevailing practice of using displacements of the material points. We conclude that the presented experimental setup and computational pipeline is a viable method that deserves wider application.
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Affiliation(s)
- Oyvind Nordbø
- Department of Mathematical Sciences and Technology, Centre for Integrative Genetics, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
| | - Pablo Lamata
- Department of Biomedical Engineering, King's College London, St. Thomas׳ Hospital, Westminster Bridge Road, London SE17EH, UK
| | - Sander Land
- Department of Biomedical Engineering, King's College London, St. Thomas׳ Hospital, Westminster Bridge Road, London SE17EH, UK
| | - Steven Niederer
- Department of Biomedical Engineering, King's College London, St. Thomas׳ Hospital, Westminster Bridge Road, London SE17EH, UK
| | - Jan M Aronsen
- Institute for Experimental Medical Research, Oslo University Hospital Ullevål and University of Oslo, Kirkeveien 166, 4th Floor Building 7, 0407 Oslo, Norway; Bjørknes College, Oslo, Norway
| | - William E Louch
- Institute for Experimental Medical Research, Oslo University Hospital Ullevål and University of Oslo, Kirkeveien 166, 4th Floor Building 7, 0407 Oslo, Norway; KG Jebsen Cardiac Research Center and Center for Heart Failure Research, University of Oslo, 0407 Oslo, Norway
| | - Ivar Sjaastad
- Institute for Experimental Medical Research, Oslo University Hospital Ullevål and University of Oslo, Kirkeveien 166, 4th Floor Building 7, 0407 Oslo, Norway
| | - Harald Martens
- Department of Engineering Cybernetics, Faculty of Information Technology, Mathematics and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway
| | - Arne B Gjuvsland
- Department of Animal and Aquacultural Sciences, Centre for Integrative Genetics, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway
| | - Kristin Tøndel
- Department of Biomedical Engineering, King's College London, St. Thomas׳ Hospital, Westminster Bridge Road, London SE17EH, UK
| | - Hans Torp
- Department of Circulation and Medical Imaging, Faculty of Medicine, Norwegian University of Science and Technology, Postboks 8905, Medisinsk teknisk forskningssenter, NO-7491 Trondheim, Norway
| | - Maelene Lohezic
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, University of Oxford, Welcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK
| | - Jurgen E Schneider
- Department of Circulation and Medical Imaging, Faculty of Medicine, Norwegian University of Science and Technology, Postboks 8905, Medisinsk teknisk forskningssenter, NO-7491 Trondheim, Norway
| | - Espen W Remme
- KG Jebsen Cardiac Research Center and Center for Heart Failure Research, University of Oslo, 0407 Oslo, Norway; Institute for Surgical Research, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Nicolas Smith
- Department of Biomedical Engineering, King's College London, St. Thomas׳ Hospital, Westminster Bridge Road, London SE17EH, UK
| | - Stig W Omholt
- Faculty of Medicine, Norwegian University of Science and Technology, P.O. Box 8905, N-7491 Trondheim, Norway
| | - Jon Olav Vik
- Department of Animal and Aquacultural Sciences, Centre for Integrative Genetics, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway.
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Young AA, Prince JL. Cardiovascular magnetic resonance: deeper insights through bioengineering. Annu Rev Biomed Eng 2013; 15:433-61. [PMID: 23662778 DOI: 10.1146/annurev-bioeng-071812-152346] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Heart disease is the main cause of morbidity and mortality worldwide, with coronary artery disease, diabetes, and obesity being major contributing factors. Cardiovascular magnetic resonance (CMR) can provide a wealth of quantitative information on the performance of the heart, without risk to the patient. Quantitative analyses of these data can substantially augment the diagnostic quality of CMR examinations and can lead to more effective characterization of disease and quantification of treatment benefit. This review provides an overview of the current state of the art in CMR with particular regard to the quantification of motion, both microscopic and macroscopic, and the application of bioengineering analysis for the evaluation of cardiac mechanics. We discuss the current clinical practice and the likely advances in the next 5-10 years, as well as the ways in which clinical examinations can be augmented by bioengineering analysis of strain, compliance, and stress.
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Affiliation(s)
- A A Young
- Department of Anatomy with Radiology, School of Medical Science, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand.
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Xi J, Lamata P, Niederer S, Land S, Shi W, Zhuang X, Ourselin S, Duckett SG, Shetty AK, Rinaldi CA, Rueckert D, Razavi R, Smith NP. The estimation of patient-specific cardiac diastolic functions from clinical measurements. Med Image Anal 2012; 17:133-46. [PMID: 23153619 PMCID: PMC6768802 DOI: 10.1016/j.media.2012.08.001] [Citation(s) in RCA: 85] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2011] [Revised: 07/26/2012] [Accepted: 08/14/2012] [Indexed: 01/01/2023]
Abstract
An unresolved issue in patients with diastolic dysfunction is that the estimation of myocardial stiffness cannot be decoupled from diastolic residual active tension (AT) because of the impaired ventricular relaxation during diastole. To address this problem, this paper presents a method for estimating diastolic mechanical parameters of the left ventricle (LV) from cine and tagged MRI measurements and LV cavity pressure recordings, separating the passive myocardial constitutive properties and diastolic residual AT. Dynamic C1-continuous meshes are automatically built from the anatomy and deformation captured from dynamic MRI sequences. Diastolic deformation is simulated using a mechanical model that combines passive and active material properties. The problem of non-uniqueness of constitutive parameter estimation using the well known Guccione law is characterized by reformulation of this law. Using this reformulated form, and by constraining the constitutive parameters to be constant across time points during diastole, we separate the effects of passive constitutive properties and the residual AT during diastolic relaxation. Finally, the method is applied to two clinical cases and one control, demonstrating that increased residual AT during diastole provides a potential novel index for delineating healthy and pathological cases.
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Affiliation(s)
- Jiahe Xi
- Department of Computer Science, University of Oxford, United Kingdom
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31
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Xi J, Lamata P, Lee J, Moireau P, Chapelle D, Smith N. Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced-order unscented Kalman filter. J Mech Behav Biomed Mater 2011; 4:1090-102. [PMID: 21783118 DOI: 10.1016/j.jmbbm.2011.03.018] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2010] [Revised: 02/21/2011] [Accepted: 03/15/2011] [Indexed: 11/25/2022]
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32
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Chabiniok R, Moireau P, Lesault PF, Rahmouni A, Deux JF, Chapelle D. Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model. Biomech Model Mechanobiol 2011; 11:609-30. [DOI: 10.1007/s10237-011-0337-8] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2011] [Accepted: 07/02/2011] [Indexed: 10/17/2022]
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33
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Land S, Niederer SA, Smith NP. Efficient computational methods for strongly coupled cardiac electromechanics. IEEE Trans Biomed Eng 2011; 59:1219-28. [PMID: 21303740 DOI: 10.1109/tbme.2011.2112359] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Strongly coupled cardiac electromechanical models can further our understanding of the relative importance of feedback mechanisms in the heart, but computational challenges currently remain a major obstacle, which limit their widespread use. To address this issue, we present a set of efficient computational methods including an efficient adaptive cell model integration scheme and a solution method for the monodomain equations that maintains high conduction velocity for time steps greater than 0.1 ms. We also present a novel method for increasing the efficiency of simulating electromechanical coupling, which shows a significant reduction in computational cost of the mechanical component on a personalized left ventricular geometry with an active contraction cell model reparametrized for human cells.
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Affiliation(s)
- Sander Land
- Computing Laboratory, University of Oxford, Oxford, UK.
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34
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Imperiale A, Chabiniok R, Moireau P, Chapelle D. Constitutive Parameter Estimation Methodology Using Tagged-MRI Data. FUNCTIONAL IMAGING AND MODELING OF THE HEART 2011. [DOI: 10.1007/978-3-642-21028-0_52] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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36
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Sun K, Stander N, Jhun CS, Zhang Z, Suzuki T, Wang GY, Saeed M, Wallace AW, Tseng EE, Baker AJ, Saloner D, Einstein DR, Ratcliffe MB, Guccione JM. A computationally efficient formal optimization of regional myocardial contractility in a sheep with left ventricular aneurysm. J Biomech Eng 2010; 131:111001. [PMID: 20016753 DOI: 10.1115/1.3148464] [Citation(s) in RCA: 75] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A non-invasive method for estimating regional myocardial contractility in vivo would be of great value in the design and evaluation of new surgical and medical strategies to treat and/or prevent infarction-induced heart failure. As a first step towards developing such a method, an explicit finite element (FE) model-based formal optimization of regional myocardial contractility in a sheep with left ventricular (LV) aneurysm was performed using tagged magnetic resonance (MR) images and cardiac catheterization pressures. From the tagged MR images, 3-dimensional (3D) myocardial strains, LV volumes and geometry for the animal-specific 3D FE model of the LV were calculated, while the LV pressures provided physiological loading conditions. Active material parameters (T(max_B) and T(max_R)) in the non-infarcted myocardium adjacent to the aneurysm (borderzone) and in myocardium remote from the aneurysm were estimated by minimizing the errors between FE model-predicted and measured systolic strains and LV volumes using the successive response surface method for optimization. The significant depression in optimized T(max_B) relative to T(max_R) was confirmed by direct ex vivo force measurements from skinned fiber preparations. The optimized values of T(max_B) and T(max_R) were not overly sensitive to the passive material parameters specified. The computation time of less than 5 hours associated with our proposed method for estimating regional myocardial contractility in vivo makes it a potentially very useful clinical tool.
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Affiliation(s)
- Kay Sun
- Department of Surgery, University of California, San Francisco, USA
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37
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Schmid H, Wang W, Hunter PJ, Nash MP. A finite element study of invariant-based orthotropic constitutive equations in the context of myocardial material parameter estimation. Comput Methods Biomech Biomed Engin 2010; 12:691-9. [PMID: 19639485 DOI: 10.1080/10255840902870427] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
A previous study investigated a number of invariant-based orthotropic and transversely isotropic constitutive equations for their suitability to fit three-dimensional simple shear mechanics data of passive myocardial tissue. The study was based on the assumption of a homogeneous deformation. Here, we extend the previous study by performing an inverse finite element material parameter estimation. This ensures a more realistic deformation state and material parameter estimates. The constitutive relations were compared on the basis of (i) 'goodness of fit': how well they fit a set of six shear deformation tests and (ii) 'variability': how well determined the material parameters are over the range of experiments. These criteria were utilised to discuss the advantages and disadvantages of the constitutive relations. It was found that a specific form of the polyconvex type as well as the exponential Fung-type equations were most suitable for modelling the orthotropic behaviour of myocardium under simple shear.
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Affiliation(s)
- H Schmid
- Department of Continuum Mechanics, RWTH Aachen University, Aachen, Germany.
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38
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Nordsletten DA, Niederer SA, Nash MP, Hunter PJ, Smith NP. Coupling multi-physics models to cardiac mechanics. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2009; 104:77-88. [PMID: 19917304 DOI: 10.1016/j.pbiomolbio.2009.11.001] [Citation(s) in RCA: 132] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2009] [Accepted: 11/10/2009] [Indexed: 11/18/2022]
Abstract
We outline and review the mathematical framework for representing mechanical deformation and contraction of the cardiac ventricles, and how this behaviour integrates with other processes crucial for understanding and modelling heart function. Building on general conservation principles of space, mass and momentum, we introduce an arbitrary Eulerian-Lagrangian framework governing the behaviour of both fluid and solid components. Exploiting the natural alignment of cardiac mechanical properties with the tissue microstructure, finite deformation measures and myocardial constitutive relations are referred to embedded structural axes. Coupling approaches for solving this large deformation mechanics framework with three dimensional fluid flow, coronary hemodynamics and electrical activation are described. We also discuss the potential of cardiac mechanics modelling for clinical applications.
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Affiliation(s)
- D A Nordsletten
- Computing Laboratory, University of Oxford, Oxford OX1 3QD, UK
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39
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Wang VY, Lam HI, Ennis DB, Cowan BR, Young AA, Nash MP. Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function. Med Image Anal 2009; 13:773-84. [PMID: 19664952 PMCID: PMC6467494 DOI: 10.1016/j.media.2009.07.006] [Citation(s) in RCA: 94] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2009] [Revised: 07/03/2009] [Accepted: 07/08/2009] [Indexed: 10/20/2022]
Abstract
The majority of patients with clinically diagnosed heart failure have normal systolic pump function and are commonly categorized as suffering from diastolic heart failure. The left ventricle (LV) remodels its structure and function to adapt to pathophysiological changes in geometry and loading conditions, which in turn can alter the passive ventricular mechanics. In order to better understand passive ventricular mechanics, a LV finite element (FE) model was customized to geometric data segmented from in vivo tagged magnetic resonance images (MRI) data and myofibre orientation derived from ex vivo diffusion tensor MRI (DTMRI) of a canine heart using nonlinear finite element fitting techniques. MRI tissue tagging enables quantitative evaluation of cardiac mechanical function with high spatial and temporal resolution, whilst the direction of maximum water diffusion in each voxel of a DTMRI directly corresponds to the local myocardial fibre orientation. Due to differences in myocardial geometry between in vivo and ex vivo imaging, myofibre orientations were mapped into the geometric FE model using host mesh fitting (a free form deformation technique). Pressure recordings, temporally synchronized to the tagging data, were used as the loading constraints to simulate the LV deformation during diastole. Simulation of diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI. Integrated physiological modelling of this kind will allow more insight into mechanics of the LV on an individualized basis, thereby improving our understanding of the underlying structural basis of mechanical dysfunction under pathological conditions.
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Affiliation(s)
- Vicky Y Wang
- Auckland Bioengineering Institute, University of Auckland, Level 6, UniServices House, 70 Symonds Street, Auckland 1142, New Zealand.
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40
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Schmid H, Wang YK, Ashton J, Ehret AE, Krittian SBS, Nash MP, Hunter PJ. Myocardial material parameter estimation: a comparison of invariant based orthotropic constitutive equations. Comput Methods Biomech Biomed Engin 2009; 12:283-95. [PMID: 19089682 DOI: 10.1080/10255840802459420] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
This study investigated a number of invariant based orthotropic and transversely isotropic constitutive equations for their suitability to fit three-dimensional simple shear mechanics data of passive myocardial tissue. A number of orthotropic laws based on Green strain components and one microstructurally based law have previously been investigated to fit experimental measurements of stress-strain behaviour. Here we extend this investigation to include several recently proposed functional forms, i.e. invariant based orthotropic and transversely isotropic constitutive relations. These laws were compared on the basis of (i) 'goodness of fit': how well they fit a set of six shear deformation tests, (ii) 'variability': how well determined the material parameters are over the range of experiments. These criteria were utilised to discuss the advantages and disadvantages of the constitutive laws. It was found that a specific form of the polyconvex type as well as the exponential Fung-type law from the previous study were most suitable for modelling the orthotropic behaviour of myocardium under simple shear.
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Affiliation(s)
- H Schmid
- Department of Continuum Mechanics, RWTH Aachen University, Aachen, Germany.
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41
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Bischoff JE, Drexler ES, Slifka AJ, McCowan CN. Quantifying nonlinear anisotropic elastic material properties of biological tissue by use of membrane inflation. Comput Methods Biomech Biomed Engin 2009; 12:353-69. [DOI: 10.1080/10255840802609420] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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42
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Abstract
Patients suffering from dilated cardiomyopathy or myocardial infarction can develop left ventricular (LV) diastolic impairment. The LV remodels its structure and function to adapt to pathophysiological changes in geometry and loading conditions and this remodeling process can alter the passive ventricular mechanics. In order to better understand passive ventricular mechanics, a LV finite element model was developed to incorporate physiological and mechanical information derived from in vivo magnetic resonance imaging (MRI) tissue tagging, in vivo LV cavity pressure recording and ex vivo diffusion tensor MRI (DTMRI) of a canine heart. MRI tissue tagging enables quantitative evaluation of cardiac mechanical function with high spatial and temporal resolution, whilst the direction of maximum water diffusion (the primary eigenvector) in each voxel of a DTMRI directly correlates with the myocardial fibre orientation. This model was customized to the geometry of the canine LV during diastasis by fitting the segmented epicardial and endocardial surface data from tagged MRI using nonlinear finite element fitting techniques. Myofibre orientations, extracted from DTMRI of the same heart, were incorporated into this geometric model using a free form deformation methodology. Pressure recordings, temporally synchronized to the tissue tagging MRI data, were used to simulate the LV deformation during diastole. Simulation of the diastolic LV mechanics allowed us to estimate the stiffness of the passive LV myocardium based on kinematic data obtained from tagged MRI. This integrated physiological model will allow more insight into the regional passive diastolic mechanics of the LV on an individualized basis, thereby improving our understanding of the underlying structural basis of mechanical dysfunction in pathological conditions.
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43
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Young A, Wang VY, Lam HI, Ennis D, Cowan B, Nash M. 311 Finite element modeling integration of cardiac MRI structure and function. J Cardiovasc Magn Reson 2008. [DOI: 10.1186/1532-429x-10-s1-a114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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44
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Estimating material parameters of human skin in vivo. Biomech Model Mechanobiol 2007; 8:1-8. [PMID: 18040732 DOI: 10.1007/s10237-007-0112-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2006] [Accepted: 11/06/2007] [Indexed: 10/22/2022]
Abstract
An accurate mathematical representation of the mechanical behaviour of human skin is essential when simulating deformations occurring in the skin during body movements or clinical procedures. In this study constitutive stress-strain relationships based on experimental data from human skin in vivo were obtained. A series of multiaxial loading experiments were performed on the forearms of four age- and gender matched subjects. The tissue geometry, together with recorded displacements and boundary forces, were combined in an analysis using finite element modelling. A non-linear optimization technique was developed to estimate values for the material parameters of a previously published constitutive law, describing the stress-strain relationship as a non-linear anisotropic membrane. Ten sets of material parameters where estimated from the experiments, showing considerable differences in mechanical behaviour both between individual subjects as well as mirrored body locations on a single subject. The accuracy of applications that simulate large deformations of human skin could be improved by using the parameters found from the in vivo experiments as described in this study.
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45
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Augenstein KF, Cowan BR, LeGrice IJ, Young AA. Estimation of cardiac hyperelastic material properties from MRI tissue tagging and diffusion tensor imaging. ACTA ACUST UNITED AC 2007; 9:628-35. [PMID: 17354943 DOI: 10.1007/11866565_77] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
The passive material properties of myocardium are important in the understanding of diastolic cardiac dysfunction. We determined hyperelastic myocardial material parameters in four isolated arrested pig hearts undergoing passive inflation of the left ventricle. Using geometry from MRI, recorded boundary conditions, muscle fiber architecture from diffusion tensor imaging, and deformation from tissue tagging, finite element models were constructed to solve the finite elasticity stress estimation problem. The constitutive parameters of a hyperelastic transversely isotropic material law were determined by minimizing the difference between the predicted and imaged deformation field. The optimized parameters were in a similar range as those reported by previous studies, showing increased passive stiffness in the muscle fiber direction. The average RMS error was 0.92 mm, similar to the image resolution of 0.80 mm. Optimization of hyperelastic models of myocardial mechanics can thus be performed to extract meaningful biophysical parameters from MRI data.
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46
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Chung JH, Rajagopal V, Nielsen PMF, Nash MP. A biomechanical model of mammographic compressions. Biomech Model Mechanobiol 2007; 7:43-52. [PMID: 17211616 DOI: 10.1007/s10237-006-0074-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2005] [Accepted: 12/10/2006] [Indexed: 10/23/2022]
Abstract
A number of biomechanical models have been proposed to improve nonrigid registration techniques for multimodal breast image alignment. A deformable breast model may also be useful for overcoming difficulties in interpreting 2D X-ray projections (mammograms) of 3D volumes (breast tissues). If a deformable model could accurately predict the shape changes that breasts undergo during mammography, then the model could serve to localize suspicious masses (visible in mammograms) in the unloaded state, or in any other deformed state required for further investigations (such as biopsy or other medical imaging modalities). In this paper, we present a validation study that was conducted in order to develop a biomechanical model based on the well-established theory of continuum mechanics (finite elasticity theory with contact mechanics) and demonstrate its use for this application. Experimental studies using gel phantoms were conducted to test the accuracy in predicting mammographic-like deformations. The material properties of the gel phantom were estimated using a nonlinear optimization process, which minimized the errors between the experimental and the model-predicted surface data by adjusting the parameter associated with the neo-Hookean constitutive relation. Two compressions (the equivalent of cranio-caudal and medio-lateral mammograms) were performed on the phantom, and the corresponding deformations were recorded using a MRI scanner. Finite element simulations were performed to mimic the experiments using the estimated material properties with appropriate boundary conditions. The simulation results matched the experimental recordings of the deformed phantom, with a sub-millimeter root-mean-square error for each compression state. Having now validated our finite element model of breast compression, the next stage is to apply the model to clinical images.
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Affiliation(s)
- J H Chung
- Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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47
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Schmid H, Nash MP, Young AA, Röhrle O, Hunter PJ. A Computationally Efficient Optimization Kernel for Material Parameter Estimation Procedures. J Biomech Eng 2006; 129:279-83. [PMID: 17408333 DOI: 10.1115/1.2540860] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Abstract
Estimating material parameters is an important part in the study of soft tissue mechanics. Computational time can easily run to days, especially when all available experimental data are taken into account. The material parameter estimation procedure is examplified on a set of homogeneous simple shear experiments to estimate the orthotropic constitutive parameters of myocardium. The modification consists of changing the traditional least-squares approach to a weighted least-squares. This objective function resembles a L2-norm type integral which is approximated using Gaussian quadrature. This reduces the computational time of the material parameter estimation by two orders of magnitude.
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Affiliation(s)
- H Schmid
- Bioengineering Institute, University of Auckland, Private Bag 92019, Auckland, 1001 New Zealand.
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48
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Mandapaka S, Hundley WG. Dobutamine cardiovascular magnetic resonance: A review. J Magn Reson Imaging 2006; 24:499-512. [PMID: 16892202 DOI: 10.1002/jmri.20678] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Dobutamine cardiovascular magnetic resonance (DCMR) is useful for identifying myocardial ischemia and viability in patients with known or suspected coronary artery disease (CAD). This article reviews the performance and utility of DCMR, its association with dobutamine stress echocardiography (DSE), and areas of active investigative research.
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Affiliation(s)
- Sangeeta Mandapaka
- Cardiology Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157, USA
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