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Lazarus A, Gao H, Luo X, Husmeier D. Improving cardio‐mechanic inference by combining in vivo strain data with ex vivo volume–pressure data. J R Stat Soc Ser C Appl Stat 2022. [DOI: 10.1111/rssc.12560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - Hao Gao
- University of Glasgow GlasgowUK
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Xu J, Wong TC, Simon MA, Brigham JC. A clinically applicable strategy to estimate the in vivo distribution of mechanical material properties of the right ventricular wall. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3548. [PMID: 34724355 DOI: 10.1002/cnm.3548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 10/27/2021] [Indexed: 06/13/2023]
Abstract
A clinically applicable approach to estimate the in vivo mechanical material properties of the heart wall is presented. This optimization-based inverse estimation approach applies a shape-based objective functional combined with rigid body registration and incremental parameterization of heterogeneity to use standard clinical imaging data along with simplified representations of cardiac function to provide consistent and physically meaningful solution estimates. The capability of the inverse estimation algorithm is evaluated through application to two clinically obtained human datasets to estimate the passive elastic mechanical properties of the heart wall, with an emphasis on the right ventricle. One dataset corresponded to a subject with normal heart function, while the other corresponded to a subject with severe pulmonary hypertension, and therefore expected to have a substantially stiffer right ventricle. Patient-specific pressure-driven bi-ventricle finite element analysis was used as the forward model and the endocardial surface of the right ventricle was used as the target data for the inverse problem. By using the right ventricle alone as the target of the inverse problem the relative sensitivity of the objective function to the right ventricle properties is increased. The method was able to identify material properties to accurately match the corresponding shape of the simplified forward model to the clinically obtained target data, and the properties obtained for the example cases are consistent with the clinical expectation for the right ventricle. Additionally, the material property estimates indicate significant heterogeneity in the heart wall for both subjects, and more so for the subject with pulmonary hypertension.
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Affiliation(s)
- Jing Xu
- Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Timothy C Wong
- UPMC Cardiovascular Magnetic Resonance Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Marc A Simon
- Department of Medicine, Division of Cardiology, University of California, San Francisco, California, USA
| | - John C Brigham
- Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Miller CE, Jordan JH, Thomas A, Weis JA. Developing a biomechanical model-based elasticity imaging method for assessing hormone receptor positive breast cancer treatment-related myocardial stiffness changes. J Med Imaging (Bellingham) 2021; 8:056002. [PMID: 34604442 DOI: 10.1117/1.jmi.8.5.056002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/16/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: Assessing cardiotoxicity as a result of breast cancer therapeutics is increasingly important as breast cancer diagnoses are trending younger and overall survival is increasing. With evidence showing that prevention of cardiotoxicity plays a significant role in increasing overall survival, there is an unmet need for accurate non-invasive methods to assess cardiac injury due to cancer therapies. Current clinical methods are too coarse and emerging research methods have not yet achieved clinical implementation. Approach: As a proof of concept, we examine myocardial elasticity imaging in the setting of premenopausal women diagnosed with hormone receptor positive (HR-positive) breast cancer undergoing severe estrogen depletion, as cardiovascular injury from early estrogen depletion is well-established. We evaluate the ability of our model-based cardiac elasticity imaging analysis method to indicate subclinical cancer therapy-related cardiac decline by examining differences in the change in cardiac elasticity over time in two cohorts of premenopausal women either undergoing severe estrogen depletion for HR-positive breast cancer or triple negative breast cancer patients as comparators. Results: Our method was capable of producing functional mechanical elasticity maps of the left ventricle (LV). Using these elasticity maps, we show significant differences in cardiac mechanical elasticity in the HR-positive breast cancer cohort compared to the comparator cohort. Conclusions: We present our methodology to assess the mechanical stiffness of the LV by interrogating cardiac magnetic resonance images within a computational biomechanical model. Our preliminary study suggests the potential of this method for examining cardiac tissue mechanical stiffness properties as an early indicator of cardiac decline.
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Affiliation(s)
- Caroline E Miller
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston-Salem, North Carolina, United States.,Virginia Tech-Wake Forest University, School of Biomedical Engineering and Sciences, Blacksburg, Virginia, United States
| | - Jennifer H Jordan
- Virginia Commonwealth University, Biomedical Engineering and Pauley Heart Center, Richmond, Virginia, United States
| | - Alexandra Thomas
- Wake Forest Baptist Medical Center, Comprehensive Cancer Center, Winston-Salem, North Carolina, United States.,Wake Forest Baptist Medical Center, Hematology and Oncology Cancer Center, Winston-Salem, North Carolina, United States
| | - Jared A Weis
- Wake Forest School of Medicine, Department of Biomedical Engineering, Winston-Salem, North Carolina, United States.,Virginia Tech-Wake Forest University, School of Biomedical Engineering and Sciences, Blacksburg, Virginia, United States.,Wake Forest Baptist Medical Center, Comprehensive Cancer Center, Winston-Salem, North Carolina, United States
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Romaszko L, Borowska A, Lazarus A, Dalton D, Berry C, Luo X, Husmeier D, Gao H. Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. Artif Intell Med 2021; 119:102140. [PMID: 34531009 DOI: 10.1016/j.artmed.2021.102140] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 06/10/2021] [Accepted: 08/03/2021] [Indexed: 12/24/2022]
Abstract
Combining biomechanical modelling of left ventricular (LV) function and dysfunction with cardiac magnetic resonance (CMR) imaging has the potential to improve the prognosis of patient-specific cardiovascular disease risks. Biomechanical studies of LV function in three dimensions usually rely on a computerized representation of the LV geometry based on finite element discretization, which is essential for numerically simulating in vivo cardiac dynamics. Detailed knowledge of the LV geometry is also relevant for various other clinical applications, such as assessing the LV cavity volume and wall thickness. Accurately and automatically reconstructing personalized LV geometries from conventional CMR images with minimal manual intervention is still a challenging task, which is a pre-requisite for any subsequent automated biomechanical analysis. We propose a deep learning-based automatic pipeline for predicting the three-dimensional LV geometry directly from routinely-available CMR cine images, without the need to manually annotate the ventricular wall. Our framework takes advantage of a low-dimensional representation of the high-dimensional LV geometry based on principal component analysis. We analyze how the inference of myocardial passive stiffness is affected by using our automatically generated LV geometries instead of manually generated ones. These insights will inform the development of statistical emulators of LV dynamics to avoid computationally expensive biomechanical simulations. Our proposed framework enables accurate LV geometry reconstruction, outperforming previous approaches by delivering a reconstruction error 50% lower than reported in the literature. We further demonstrate that for a nonlinear cardiac mechanics model, using our reconstructed LV geometries instead of manually extracted ones only moderately affects the inference of passive myocardial stiffness described by an anisotropic hyperelastic constitutive law. The developed methodological framework has the potential to make an important step towards personalized medicine by eliminating the need for time consuming and costly manual operations. In addition, our method automatically maps the CMR scan into a low-dimensional representation of the LV geometry, which constitutes an important stepping stone towards the development of an LV geometry-heterogeneous emulator.
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Affiliation(s)
- Lukasz Romaszko
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Agnieszka Borowska
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Alan Lazarus
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - David Dalton
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Colin Berry
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Xiaoyu Luo
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Dirk Husmeier
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK
| | - Hao Gao
- School of Mathematics and Statistics, Univeristy of Glasgow, Glasgow, UK.
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Fan Y, Coll-Font J, van den Boomen M, Kim JH, Chen S, Eder RA, Roche ET, Nguyen CT. Characterization of Exercise-Induced Myocardium Growth Using Finite Element Modeling and Bayesian Optimization. Front Physiol 2021; 12:694940. [PMID: 34434115 PMCID: PMC8381603 DOI: 10.3389/fphys.2021.694940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/19/2021] [Indexed: 02/03/2023] Open
Abstract
Cardiomyocyte growth can occur in both physiological (exercised-induced) and pathological (e.g., volume overload and pressure overload) conditions leading to left ventricular (LV) hypertrophy. Studies using animal models and histology have demonstrated the growth and remodeling process at the organ level and tissue-cellular level, respectively. However, the driving factors of growth and the mechanistic link between organ, tissue, and cellular growth remains poorly understood. Computational models have the potential to bridge this gap by using constitutive models that describe the growth and remodeling process of the myocardium coupled with finite element (FE) analysis to model the biomechanics of the heart at the organ level. Using subject-specific imaging data of the LV geometry at two different time points, an FE model can be created with the inverse method to characterize the growth parameters of each subject. In this study, we developed a framework that takes in vivo cardiac magnetic resonance (CMR) imaging data of exercised porcine model and uses FE and Bayesian optimization to characterize myocardium growth in the transverse and longitudinal directions. The efficacy of this framework was demonstrated by successfully predicting growth parameters of 18 synthetic LV targeted masks which were generated from three LV porcine geometries. The framework was further used to characterize growth parameters in 4 swine subjects that had been exercised. The study suggested that exercise-induced growth in swine is prone to longitudinal cardiomyocyte growth (58.0 ± 19.6% after 6 weeks and 79.3 ± 15.6% after 12 weeks) compared to transverse growth (4.0 ± 8.0% after 6 weeks and 7.8 ± 9.4% after 12 weeks). This framework can be used to characterize myocardial growth in different phenotypes of LV hypertrophy and can be incorporated with other growth constitutive models to study different hypothetical growth mechanisms.
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Affiliation(s)
- Yiling Fan
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jaume Coll-Font
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States,Harvard Medical School, Boston, MA, United States
| | - Maaike van den Boomen
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States,Harvard Medical School, Boston, MA, United States
| | - Joan H. Kim
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States
| | - Shi Chen
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States
| | - Robert Alan Eder
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States
| | - Ellen T. Roche
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States,Harvard Medical School, Boston, MA, United States,*Correspondence: Ellen T. Roche,
| | - Christopher T. Nguyen
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States,Harvard Medical School, Boston, MA, United States,Christopher T. Nguyen,
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Hadjicharalambous M, Stoeck CT, Weisskopf M, Cesarovic N, Ioannou E, Vavourakis V, Nordsletten DA. Investigating the reference domain influence in personalised models of cardiac mechanics : Effect of unloaded geometry on cardiac biomechanics. Biomech Model Mechanobiol 2021; 20:1579-1597. [PMID: 34047891 DOI: 10.1007/s10237-021-01464-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 05/03/2021] [Indexed: 01/23/2023]
Abstract
A major concern in personalised models of heart mechanics is the unknown zero-pressure domain, a prerequisite for accurately predicting cardiac biomechanics. As the reference configuration cannot be captured by clinical data, studies often employ in-vivo frames which are unlikely to correspond to unloaded geometries. Alternatively, zero-pressure domain is approximated through inverse methodologies, which, however, entail assumptions pertaining to boundary conditions and material parameters. Both approaches are likely to introduce biases in estimated biomechanical properties; nevertheless, quantification of these effects is unattainable without ground-truth data. In this work, we assess the unloaded state influence on model-derived biomechanics, by employing an in-silico modelling framework relying on experimental data on porcine hearts. In-vivo images are used for model personalisation, while in-situ experiments provide a reliable approximation of the reference domain, creating a unique opportunity for a validation study. Personalised whole-cycle cardiac models are developed which employ different reference domains (image-derived, inversely estimated) and are compared against ground-truth model outcomes. Simulations are conducted with varying boundary conditions, to investigate the effect of data-derived constraints on model accuracy. Attention is given to modelling the influence of the ribcage on the epicardium, due to its close proximity to the heart in the porcine anatomy. Our results find merit in both approaches for dealing with the unknown reference domain, but also demonstrate differences in estimated biomechanical quantities such as material parameters, strains and stresses. Notably, they highlight the importance of a boundary condition accounting for the constraining influence of the ribcage, in forward and inverse biomechanical models.
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Affiliation(s)
| | - Christian T Stoeck
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Miriam Weisskopf
- Center for Surgical Research, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Nikola Cesarovic
- Center for Surgical Research, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Translational Cardiovascular Technologies, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.,Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Eleftherios Ioannou
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus
| | - Vasileios Vavourakis
- Department of Mechanical and Manufacturing Engineering, University of Cyprus, Nicosia, Cyprus.,Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - David A Nordsletten
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Department of Biomedical Engineering and Cardiac Surgery, University of Michigan, Ann Arbor, MI, USA
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