1
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Rodero C, Baptiste TMG, Barrows RK, Lewalle A, Niederer SA, Strocchi M. Advancing clinical translation of cardiac biomechanics models: a comprehensive review, applications and future pathways. FRONTIERS IN PHYSICS 2023; 11:1306210. [PMID: 38500690 PMCID: PMC7615748 DOI: 10.3389/fphy.2023.1306210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Cardiac mechanics models are developed to represent a high level of detail, including refined anatomies, accurate cell mechanics models, and platforms to link microscale physiology to whole-organ function. However, cardiac biomechanics models still have limited clinical translation. In this review, we provide a picture of cardiac mechanics models, focusing on their clinical translation. We review the main experimental and clinical data used in cardiac models, as well as the steps followed in the literature to generate anatomical meshes ready for simulations. We describe the main models in active and passive mechanics and the different lumped parameter models to represent the circulatory system. Lastly, we provide a summary of the state-of-the-art in terms of ventricular, atrial, and four-chamber cardiac biomechanics models. We discuss the steps that may facilitate clinical translation of the biomechanics models we describe. A well-established software to simulate cardiac biomechanics is lacking, with all available platforms involving different levels of documentation, learning curves, accessibility, and cost. Furthermore, there is no regulatory framework that clearly outlines the verification and validation requirements a model has to satisfy in order to be reliably used in applications. Finally, better integration with increasingly rich clinical and/or experimental datasets as well as machine learning techniques to reduce computational costs might increase model reliability at feasible resources. Cardiac biomechanics models provide excellent opportunities to be integrated into clinical workflows, but more refinement and careful validation against clinical data are needed to improve their credibility. In addition, in each context of use, model complexity must be balanced with the associated high computational cost of running these models.
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Affiliation(s)
- Cristobal Rodero
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Tiffany M. G. Baptiste
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Rosie K. Barrows
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Alexandre Lewalle
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Steven A. Niederer
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
- Turing Research and Innovation Cluster in Digital Twins (TRIC: DT), The Alan Turing Institute, London, United Kingdom
| | - Marina Strocchi
- Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
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2
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He J, Pertsov AM, Cherry EM, Fenton FH, Roney CH, Niederer SA, Zang Z, Mangharam R. Fiber Organization has Little Effect on Electrical Activation Patterns during Focal Arrhythmias in the Left Atrium. ARXIV 2023:arXiv:2210.16497v3. [PMID: 36776816 PMCID: PMC9915751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Over the past two decades there has been a steady trend towards the development of realistic models of cardiac conduction with increasing levels of detail. However, making models more realistic complicates their personalization and use in clinical practice due to limited availability of tissue and cellular scale data. One such limitation is obtaining information about myocardial fiber organization in the clinical setting. In this study, we investigated a chimeric model of the left atrium utilizing clinically derived patient-specific atrial geometry and a realistic, yet foreign for a given patient fiber organization. We discovered that even significant variability of fiber organization had a relatively small effect on the spatio-temporal activation pattern during regular pacing. For a given pacing site, the activation maps were very similar across all fiber organizations tested.
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Affiliation(s)
- Jiyue He
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
| | | | - Elizabeth M Cherry
- School of Computational Science and Engineering, Georgia Institute of Technology, USA
| | | | - Caroline H Roney
- School of Engineering and Materials Science, Queen Mary University of London, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Zirui Zang
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
| | - Rahul Mangharam
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
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3
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Galappaththige S, Gray RA, Costa CM, Niederer S, Pathmanathan P. Credibility assessment of patient-specific computational modeling using patient-specific cardiac modeling as an exemplar. PLoS Comput Biol 2022; 18:e1010541. [PMID: 36215228 PMCID: PMC9550052 DOI: 10.1371/journal.pcbi.1010541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 09/02/2022] [Indexed: 11/07/2022] Open
Abstract
Reliable and robust simulation of individual patients using patient-specific models (PSMs) is one of the next frontiers for modeling and simulation (M&S) in healthcare. PSMs, which form the basis of digital twins, can be employed as clinical tools to, for example, assess disease state, predict response to therapy, or optimize therapy. They may also be used to construct virtual cohorts of patients, for in silico evaluation of medical product safety and/or performance. Methods and frameworks have recently been proposed for evaluating the credibility of M&S in healthcare applications. However, such efforts have generally been motivated by models of medical devices or generic patient models; how best to evaluate the credibility of PSMs has largely been unexplored. The aim of this paper is to understand and demonstrate the credibility assessment process for PSMs using patient-specific cardiac electrophysiological (EP) modeling as an exemplar. We first review approaches used to generate cardiac PSMs and consider how verification, validation, and uncertainty quantification (VVUQ) apply to cardiac PSMs. Next, we execute two simulation studies using a publicly available virtual cohort of 24 patient-specific ventricular models, the first a multi-patient verification study, the second investigating the impact of uncertainty in personalized and non-personalized inputs in a virtual cohort. We then use the findings from our analyses to identify how important characteristics of PSMs can be considered when assessing credibility with the approach of the ASME V&V40 Standard, accounting for PSM concepts such as inter- and intra-user variability, multi-patient and “every-patient” error estimation, uncertainty quantification in personalized vs non-personalized inputs, clinical validation, and others. The results of this paper will be useful to developers of cardiac and other medical image based PSMs, when assessing PSM credibility. Patient-specific models are computational models that have been personalized using data from a patient. After decades of research, recent computational, data science and healthcare advances have opened the door to the fulfilment of the enormous potential of such models, from truly personalized medicine to efficient and cost-effective testing of new medical products. However, reliability (credibility) of patient-specific models is key to their success, and there are currently no general guidelines for evaluating credibility of patient-specific models. Here, we consider how frameworks and model evaluation activities that have been developed for generic (not patient-specific) computational models, can be extended to patient specific models. We achieve this through a detailed analysis of the activities required to evaluate cardiac electrophysiological models, chosen as an exemplar field due to its maturity and the complexity of such models. This is the first paper on the topic of reliability of patient-specific models and will help pave the way to reliable and trusted patient-specific modeling across healthcare applications.
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Affiliation(s)
- Suran Galappaththige
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Richard A. Gray
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Caroline Mendonca Costa
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Steven Niederer
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland, United States of America
- * E-mail:
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4
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Sahal R, Alsamhi SH, Brown KN. Personal Digital Twin: A Close Look into the Present and a Step towards the Future of Personalised Healthcare Industry. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22155918. [PMID: 35957477 PMCID: PMC9371419 DOI: 10.3390/s22155918] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/22/2022] [Accepted: 08/01/2022] [Indexed: 05/12/2023]
Abstract
Digital twins (DTs) play a vital role in revolutionising the healthcare industry, leading to more personalised, intelligent, and proactive healthcare. With the evolution of personalised healthcare, there is a significant need to represent a virtual replica for individuals to provide the right type of care in the right way and at the right time. Therefore, in this paper, we surveyed the concept of a personal digital twin (PDT) as an enhanced version of the DT with actionable insight capabilities. In particular, PDT can bring value to patients by enabling more accurate decision making and proper treatment selection and optimisation. Then, we explored the progression of PDT as a revolutionary technology in healthcare research and industry. However, although several research works have been performed for smart healthcare using DT, PDT is still at an early stage. Consequently, we believe that this work can be a step towards smart personalised healthcare industry by guiding the design of industrial personalised healthcare systems. Accordingly, we introduced a reference framework that empowers smart personalised healthcare using PDTs by bringing together existing advanced technologies (i.e., DT, blockchain, and AI). Then, we described some selected use cases, including the mitigation of COVID-19 contagion, COVID-19 survivor follow-up care, personalised COVID-19 medicine, personalised osteoporosis prevention, personalised cancer survivor follow-up care, and personalised nutrition. Finally, we identified further challenges to pave the PDT paradigm toward the smart personalised healthcare industry.
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Affiliation(s)
- Radhya Sahal
- School of Computer Science and Information Technology, University College Cork, T12 E8YV Cork, Ireland
- Correspondence: (R.S.); (K.N.B.)
| | - Saeed H. Alsamhi
- Insight Centre for Data Analytics, National University of Ireland, N37 W089 Galway, Ireland
- Faculty of Engineering, IBB University, Ibb 70270, Yemen
| | - Kenneth N. Brown
- School of Computer Science and Information Technology, University College Cork, T12 E8YV Cork, Ireland
- Correspondence: (R.S.); (K.N.B.)
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5
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Heidari A, Elkhodary KI, Pop C, Badran M, Vali H, Abdel-Raouf YMA, Torbati S, Asgharian M, Steele RJ, Mahmoudzadeh Kani I, Sheibani S, Pouraliakbar H, Sadeghian H, Cecere R, Friedrich MGW, Tafti HA. Patient-specific finite element analysis of heart failure and the impact of surgical intervention in pulmonary hypertension secondary to mitral valve disease. Med Biol Eng Comput 2022; 60:1723-1744. [PMID: 35442004 DOI: 10.1007/s11517-022-02556-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 03/12/2022] [Indexed: 12/31/2022]
Abstract
Pulmonary hypertension (PH), a chronic and complex medical condition affecting 1% of the global population, requires clinical evaluation of right ventricular maladaptation patterns under various conditions. A particular challenge for clinicians is a proper quantitative assessment of the right ventricle (RV) owing to its intimate coupling to the left ventricle (LV). We, thus, proposed a patient-specific computational approach to simulate PH caused by left heart disease and its main adverse functional and structural effects on the whole heart. Information obtained from both prospective and retrospective studies of two patients with severe PH, a 72-year-old female and a 61-year-old male, is used to present patient-specific versions of the Living Heart Human Model (LHHM) for the pre-operative and post-operative cardiac surgery. Our findings suggest that before mitral and tricuspid valve repair, the patients were at risk of right ventricular dilatation which may progress to right ventricular failure secondary to their mitral valve disease and left ventricular dysfunction. Our analysis provides detailed evidence that mitral valve replacement and subsequent chamber pressure unloading are associated with a significant decrease in failure risk post-operatively in the context of pulmonary hypertension. In particular, right-sided strain markers, such as tricuspid annular plane systolic excursion (TAPSE) and circumferential and longitudinal strains, indicate a transition from a range representative of disease to within typical values after surgery. Furthermore, the wall stresses across the RV and the interventricular septum showed a notable decrease during the systolic phase after surgery, lessening the drive for further RV maladaptation and significantly reducing the risk of RV failure.
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Affiliation(s)
- Alireza Heidari
- Department of Mechanical Engineering, McGill University, 817 Sherbrooke Street West, Montreal, QC, H3A 0C3, Canada. .,Department of Anatomy & Cell Biology, McGill University, Montreal, QC, Canada.
| | - Khalil I Elkhodary
- Department of Mechanical Engineering, American University in Cairo, New Cairo, 11835, Egypt
| | - Cristina Pop
- Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Mohamed Badran
- Department of Mechanical Engineering, Future University in Egypt, New Cairo, Egypt
| | - Hojatollah Vali
- Department of Anatomy & Cell Biology, McGill University, Montreal, QC, Canada
| | - Yousof M A Abdel-Raouf
- Department of Mechanical Engineering, American University in Cairo, New Cairo, 11835, Egypt
| | - Saeed Torbati
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Masoud Asgharian
- Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada
| | - Russell J Steele
- Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada
| | | | - Sara Sheibani
- Department of Anatomy & Cell Biology, McGill University, Montreal, QC, Canada
| | - Hamidreza Pouraliakbar
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Hakimeh Sadeghian
- Faculty of Medicine, Tehran University of Medical Science, Tehran, Iran.,Department of Surgery, Tehran Heart Center, Tehran, Iran
| | - Renzo Cecere
- Department of Mechanical Engineering, McGill University, 817 Sherbrooke Street West, Montreal, QC, H3A 0C3, Canada.,Department of Surgery, Royal Victoria Hospital, McGill University Health Centre, Montreal, QC, Canada
| | - Matthias G W Friedrich
- Departments of Medicine and Diagnostic Radiology, McGill University, Montreal, QC, Canada
| | - Hossein Ahmadi Tafti
- Faculty of Medicine, Tehran University of Medical Science, Tehran, Iran.,Department of Surgery, Tehran Heart Center, Tehran, Iran
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6
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Wu C, Lorenzo G, Hormuth DA, Lima EABF, Slavkova KP, DiCarlo JC, Virostko J, Phillips CM, Patt D, Chung C, Yankeelov TE. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. BIOPHYSICS REVIEWS 2022; 3:021304. [PMID: 35602761 PMCID: PMC9119003 DOI: 10.1063/5.0086789] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 04/29/2022] [Indexed: 12/11/2022]
Abstract
Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.
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Affiliation(s)
- Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | | | - Kalina P. Slavkova
- Department of Physics, The University of Texas at Austin, Austin, Texas 78712, USA
| | | | | | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
| | - Debra Patt
- Texas Oncology, Austin, Texas 78731, USA
| | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, University of Texas, Houston, Texas 77030, USA
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7
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Stimm J, Guenthner C, Kozerke S, Stoeck CT. Comparison of interpolation methods of predominant cardiomyocyte orientation from in vivo and ex vivo cardiac diffusion tensor imaging data. NMR IN BIOMEDICINE 2022; 35:e4667. [PMID: 34964179 PMCID: PMC9285076 DOI: 10.1002/nbm.4667] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/25/2021] [Accepted: 11/26/2021] [Indexed: 06/14/2023]
Abstract
Cardiac electrophysiology and cardiac mechanics both depend on the average cardiomyocyte long-axis orientation. In the realm of personalized medicine, knowledge of the patient-specific changes in cardiac microstructure plays a crucial role. Patient-specific computational modelling has emerged as a tool to better understand disease progression. In vivo cardiac diffusion tensor imaging (cDTI) is a vital tool to non-destructively measure the average cardiomyocyte long-axis orientation in the heart. However, cDTI suffers from long scan times, rendering volumetric, high-resolution acquisitions challenging. Consequently, interpolation techniques are needed to populate bio-mechanical models with patient-specific average cardiomyocyte long-axis orientations. In this work, we compare five interpolation techniques applied to in vivo and ex vivo porcine input data. We compare two tensor interpolation approaches, one rule-based approximation, and two data-driven, low-rank models. We demonstrate the advantage of tensor interpolation techniques, resulting in lower interpolation errors than do low-rank models and rule-based methods adapted to cDTI data. In an ex vivo comparison, we study the influence of three imaging parameters that can be traded off against acquisition time: in-plane resolution, signal to noise ratio, and number of acquired short-axis imaging slices.
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Affiliation(s)
- Johanna Stimm
- Institute for Biomedical EngineeringUniversity and ETH ZurichZurichSwitzerland
| | - Christian Guenthner
- Institute for Biomedical EngineeringUniversity and ETH ZurichZurichSwitzerland
| | - Sebastian Kozerke
- Institute for Biomedical EngineeringUniversity and ETH ZurichZurichSwitzerland
| | - Christian T. Stoeck
- Institute for Biomedical EngineeringUniversity and ETH ZurichZurichSwitzerland
- Division of Surgical ResearchUniversity Hospital ZurichUniversity ZurichSwitzerland
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8
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Jung A, Gsell MAF, Augustin CM, Plank G. An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics—A Multi-Fidelity Approach for Personalising Active Mechanics. MATHEMATICS 2022; 10:823. [PMID: 35295404 PMCID: PMC7612499 DOI: 10.3390/math10050823] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Personalised computer models of cardiac function, referred to as cardiac digital twins, are envisioned to play an important role in clinical precision therapies of cardiovascular diseases. A major obstacle hampering clinical translation involves the significant computational costs involved in the personalisation of biophysically detailed mechanistic models that require the identification of high-dimensional parameter vectors. An important aspect to identify in electromechanics (EM) models are active mechanics parameters that govern cardiac contraction and relaxation. In this study, we present a novel, fully automated, and efficient approach for personalising biophysically detailed active mechanics models using a two-step multi-fidelity solution. In the first step, active mechanical behaviour in a given 3D EM model is represented by a purely phenomenological, low-fidelity model, which is personalised at the organ scale by calibration to clinical cavity pressure data. Then, in the second step, median traces of nodal cellular active stress, intracellular calcium concentration, and fibre stretch are generated and utilised to personalise the desired high-fidelity model at the cellular scale using a 0D model of cardiac EM. Our novel approach was tested on a cohort of seven human left ventricular (LV) EM models, created from patients treated for aortic coarctation (CoA). Goodness of fit, computational cost, and robustness of the algorithm against uncertainty in the clinical data and variations of initial guesses were evaluated. We demonstrate that our multi-fidelity approach facilitates the personalisation of a biophysically detailed active stress model within only a few (2 to 4) expensive 3D organ-scale simulations—a computational effort compatible with clinical model applications.
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Affiliation(s)
- Alexander Jung
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
| | - Matthias A. F. Gsell
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- NAWI Graz, Institute of Mathematics and Scientific Computing, University of Graz, 8010 Graz, Austria
| | - Christoph M. Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- BioTechMed-Graz, 8010 Graz, Austria
- Correspondence:
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging—Division of Biophysics, Medical University Graz, 8010 Graz, Austria
- BioTechMed-Graz, 8010 Graz, Austria
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9
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Mazumder O, Banerjee R, Roy D, Bhattacharya S, Ghose A, Sinha A. Synthetic PPG signal Generation to Improve Coronary Artery Disease Classification: Study with Physical Model of Cardiovascular System. IEEE J Biomed Health Inform 2022; 26:2136-2146. [PMID: 35104231 DOI: 10.1109/jbhi.2022.3147383] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This paper presents a novel approach of generating synthetic Photoplethysmogram (PPG) data using a physical model of the cardiovascular system to improve classifier performance with a combination of synthetic and real data. The physical model is an in-silico cardiac computational model, consisting of a four-chambered heart with electrophysiology, hemodynamic, and blood pressure autoregulation functionality. Starting with a small number of measured PPG data, the cardiac model is used to synthesize healthy as well as PPG time-series pertaining to coronary artery disease (CAD) by varying pathophysiological parameters. A Variational Autoencoder (VAE) structure is proposed to derive a statistical feature space for CAD classification. Results are presented in two perspectives namely, (i) using artificially reduced real disease data and (ii) using all the real disease data. In both cases, by augmenting with the synthetic data for training, the performance (sensitivity, specificity) of the classifier changes from (i) (0.65, 1) to (1, 0.9) and (ii) (1, 0.95) to (1,1). The proposed hybrid approach of combining physical modelling and statistical feature space selection generates realistic PPG data with pathophysiological interpretation and can outperform a baseline Generative Adversarial Network (GAN) architecture with a relatively small amount of real data for training. This proposed method could aid as a substitution technique for handling the problem of bulk data required for training machine learning algorithms for cardiac health-care applications.
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10
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Park J, Wu Z, Steiner PR, Zhu B, Zhang JXJ. Heart-on-Chip for Combined Cellular Dynamics Measurements and Computational Modeling Towards Clinical Applications. Ann Biomed Eng 2022; 50:111-137. [PMID: 35039976 DOI: 10.1007/s10439-022-02902-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 01/01/2022] [Indexed: 12/24/2022]
Abstract
Organ-on-chip or micro-engineered three-dimensional cellular or tissue models are increasingly implemented in the study of cardiovascular pathophysiology as alternatives to traditional in vitro cell culture. Drug induced cardiotoxicity is a key issue in drug development pipelines, but the current in vitro and in vivo studies suffer from inter-species differences, high costs, and lack of reliability and accuracy in predicting cardiotoxicity. Microfluidic heart-on-chip devices can impose a paradigm shift to the current tools. They can not only recapitulate cardiac tissue level functionality and the communication between cells and extracellular matrices but also allow higher throughput studies conducive to drug screening especially with their added functionalities or sensors that extract disease-specific phenotypic, genotypic, and electrophysiological information in real-time. Such electrical and mechanical components can tailor the electrophysiology and mechanobiology of the experiment to better mimic the in vivo condition as well. Recent advancements and challenges are reviewed in the fabrication, functionalization and sensor assisted mechanical and electrophysiological measurements, numerical and computational modeling of cardiomyocytes' behavior, and the clinical applications in drug screening and disease modeling. This review concludes with the current challenges and perspectives on the future of such organ-on-chip platforms.
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Affiliation(s)
- Jiyoon Park
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Ziqian Wu
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Paul R Steiner
- Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA
| | - Bo Zhu
- Computer Science Department, Dartmouth College, Hanover, NH, 03755, USA
| | - John X J Zhang
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA. .,Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA.
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11
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Stimm J, Nordsletten DA, Jilberto J, Miller R, Berberoğlu E, Kozerke S, Stoeck CT. Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods. Front Physiol 2022; 13:1042537. [PMID: 36518106 PMCID: PMC9742433 DOI: 10.3389/fphys.2022.1042537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/14/2022] [Indexed: 11/29/2022] Open
Abstract
Simulations of cardiac electrophysiology and mechanics have been reported to be sensitive to the microstructural anisotropy of the myocardium. Consequently, a personalized representation of cardiac microstructure is a crucial component of accurate, personalized cardiac biomechanical models. In-vivo cardiac Diffusion Tensor Imaging (cDTI) is a non-invasive magnetic resonance imaging technique capable of probing the heart's microstructure. Being a rather novel technique, issues such as low resolution, signal-to noise ratio, and spatial coverage are currently limiting factors. We outline four interpolation techniques with varying degrees of data fidelity, different amounts of smoothing strength, and varying representation error to bridge the gap between the sparse in-vivo data and the model, requiring a 3D representation of microstructure across the myocardium. We provide a workflow to incorporate in-vivo myofiber orientation into a left ventricular model and demonstrate that personalized modelling based on fiber orientations from in-vivo cDTI data is feasible. The interpolation error is correlated with a trend in personalized parameters and simulated physiological parameters, strains, and ventricular twist. This trend in simulation results is consistent across material parameter settings and therefore corresponds to a bias introduced by the interpolation method. This study suggests that using a tensor interpolation approach to personalize microstructure with in-vivo cDTI data, reduces the fiber uncertainty and thereby the bias in the simulation results.
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Affiliation(s)
- Johanna Stimm
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - David A Nordsletten
- Department of Biomedical Engineering and Cardiac Surgery, University of Michigan, Ann Arbor, MI, United States.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Javiera Jilberto
- Department of Biomedical Engineering and Cardiac Surgery, University of Michigan, Ann Arbor, MI, United States
| | - Renee Miller
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ezgi Berberoğlu
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Christian T Stoeck
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.,Division of Surgical Research, University Hospital Zurich, University Zurich, Zurich, Switzerland
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12
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Oomen PJA, Phung TKN, Weinberg SH, Bilchick KC, Holmes JW. A rapid electromechanical model to predict reverse remodeling following cardiac resynchronization therapy. Biomech Model Mechanobiol 2021; 21:231-247. [PMID: 34816336 DOI: 10.1007/s10237-021-01532-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 10/22/2021] [Indexed: 10/19/2022]
Abstract
Cardiac resynchronization therapy (CRT) is an effective therapy for patients who suffer from heart failure and ventricular dyssynchrony such as left bundle branch block (LBBB). When it works, it reverses adverse left ventricular (LV) remodeling and the progression of heart failure. However, CRT response rate is currently as low as 50-65%. In theory, CRT outcome could be improved by allowing clinicians to tailor the therapy through patient-specific lead locations, timing, and/or pacing protocol. However, this also presents a dilemma: there are far too many possible strategies to test during the implantation surgery. Computational models could address this dilemma by predicting remodeling outcomes for each patient before the surgery takes place. Therefore, the goal of this study was to develop a rapid computational model to predict reverse LV remodeling following CRT. We adapted our recently developed computational model of LV remodeling to simulate the mechanics of ventricular dyssynchrony and added a rapid electrical model to predict electrical activation timing. The model was calibrated to quantitatively match changes in hemodynamics and global and local LV wall mass from a canine study of LBBB and CRT. The calibrated model was used to investigate the influence of LV lead location and ischemia on CRT remodeling outcome. Our model results suggest that remodeling outcome varies with both lead location and ischemia location, and does not always correlate with short-term improvement in QRS duration. The results and time frame required to customize and run this model suggest promise for this approach in a clinical setting.
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Affiliation(s)
- Pim J A Oomen
- Department of Biomedical Engineering, University of Virginia, Box 800759, Health System, Charlottesville, VA, 22903, USA.,Department of Medicine, University of Virginia, Box 800158, Health System, Charlottesville, VA, 22903, USA
| | - Thien-Khoi N Phung
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, 665 Huntington Ave, Boston, MA, 02115, USA
| | - Seth H Weinberg
- Department of Biomedical Engineering, The Ohio State University, 140 W 19th Ave Columbus, Columbus, OH, 43210, USA
| | - Kenneth C Bilchick
- Department of Medicine, University of Virginia, Box 800158, Health System, Charlottesville, VA, 22903, USA
| | - Jeffrey W Holmes
- Department of Biomedical Engineering, University of Virginia, Box 800759, Health System, Charlottesville, VA, 22903, USA. .,School of Engineering, University of Alabama at Birmingham, 1075 13th St S, Birmingham, AL, 35233, USA.
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13
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Meister F, Passerini T, Audigier C, Lluch È, Mihalef V, Ashikaga H, Maier A, Halperin H, Mansi T. Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks. Front Physiol 2021; 12:694869. [PMID: 34733172 PMCID: PMC8558498 DOI: 10.3389/fphys.2021.694869] [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: 04/13/2021] [Accepted: 09/15/2021] [Indexed: 11/25/2022] Open
Abstract
Electroanatomic mapping is the gold standard for the assessment of ventricular tachycardia. Acquiring high resolution electroanatomic maps is technically challenging and may require interpolation methods to obtain dense measurements. These methods, however, cannot recover activation times in the entire biventricular domain. This work investigates the use of graph convolutional neural networks to estimate biventricular activation times from sparse measurements. Our method is trained on more than 15,000 synthetic examples of realistic ventricular depolarization patterns generated by a computational electrophysiology model. Using geometries sampled from a statistical shape model of biventricular anatomy, diverse wave dynamics are induced by randomly sampling scar and border zone distributions, locations of initial activation, and tissue conduction velocities. Once trained, the method accurately reconstructs biventricular activation times in left-out synthetic simulations with a mean absolute error of 3.9 ms ± 4.2 ms at a sampling density of one measurement sample per cm2. The total activation time is matched with a mean error of 1.4 ms ± 1.4 ms. A significant decrease in errors is observed in all heart zones with an increased number of samples. Without re-training, the network is further evaluated on two datasets: (1) an in-house dataset comprising four ischemic porcine hearts with dense endocardial activation maps; (2) the CRT-EPIGGY19 challenge data comprising endo- and epicardial measurements of 5 infarcted and 6 non-infarcted swines. In both setups the neural network recovers biventricular activation times with a mean absolute error of less than 10 ms even when providing only a subset of endocardial measurements as input. Furthermore, we present a simple approach to suggest new measurement locations in real-time based on the estimated uncertainty of the graph network predictions. The model-guided selection of measurement locations allows to reduce by 40% the number of measurements required in a random sampling strategy, while achieving the same prediction error. In all the tested scenarios, the proposed approach estimates biventricular activation times with comparable or better performance than a personalized computational model and significant runtime advantages.
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Affiliation(s)
- Felix Meister
- Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Tiziano Passerini
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States
| | - Chloé Audigier
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Èric Lluch
- Digital Technology and Innovation, Siemens Healthineers, Erlangen, Germany
| | - Viorel Mihalef
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States
| | - Hiroshi Ashikaga
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander University, Erlangen, Germany
| | - Henry Halperin
- Cardiac Arrhythmia Service, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Tommaso Mansi
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States
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14
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Sermesant M, Delingette H, Cochet H, Jaïs P, Ayache N. Applications of artificial intelligence in cardiovascular imaging. Nat Rev Cardiol 2021; 18:600-609. [PMID: 33712806 DOI: 10.1038/s41569-021-00527-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2021] [Indexed: 01/31/2023]
Abstract
Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research into the application of AI to medical imaging is now very active, especially in the field of cardiovascular imaging because of the challenges associated with acquiring and analysing images of this dynamic organ. In this Review, we discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems. Some approaches are purely data-driven and rely mainly on statistical associations, whereas others integrate anatomical and physiological information through additional statistical, geometric and biophysical models of the human heart. In a structured manner, we provide representative examples of each of these approaches, with particular attention to the underlying computational imaging challenges. Finally, we discuss the remaining limitations of AI approaches in cardiovascular imaging (such as generalizability and explainability) and how they can be overcome.
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Affiliation(s)
| | | | - Hubert Cochet
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
| | - Pierre Jaïs
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
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15
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Khodaei S, Henstock A, Sadeghi R, Sellers S, Blanke P, Leipsic J, Emadi A, Keshavarz-Motamed Z. Personalized intervention cardiology with transcatheter aortic valve replacement made possible with a non-invasive monitoring and diagnostic framework. Sci Rep 2021; 11:10888. [PMID: 34035325 PMCID: PMC8149684 DOI: 10.1038/s41598-021-85500-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 02/12/2021] [Indexed: 02/04/2023] Open
Abstract
One of the most common acute and chronic cardiovascular disease conditions is aortic stenosis, a disease in which the aortic valve is damaged and can no longer function properly. Moreover, aortic stenosis commonly exists in combination with other conditions causing so many patients suffer from the most general and fundamentally challenging condition: complex valvular, ventricular and vascular disease (C3VD). Transcatheter aortic valve replacement (TAVR) is a new less invasive intervention and is a growing alternative for patients with aortic stenosis. Although blood flow quantification is critical for accurate and early diagnosis of C3VD in both pre and post-TAVR, proper diagnostic methods are still lacking because the fluid-dynamics methods that can be used as engines of new diagnostic tools are not well developed yet. Despite remarkable advances in medical imaging, imaging on its own is not enough to quantify the blood flow effectively. Moreover, understanding of C3VD in both pre and post-TAVR and its progression has been hindered by the absence of a proper non-invasive tool for the assessment of the cardiovascular function. To enable the development of new non-invasive diagnostic methods, we developed an innovative image-based patient-specific computational fluid dynamics framework for patients with C3VD who undergo TAVR to quantify metrics of: (1) global circulatory function; (2) global cardiac function as well as (3) local cardiac fluid dynamics. This framework is based on an innovative non-invasive Doppler-based patient-specific lumped-parameter algorithm and a 3-D strongly-coupled fluid-solid interaction. We validated the framework against clinical cardiac catheterization and Doppler echocardiographic measurements and demonstrated its diagnostic utility by providing novel analyses and interpretations of clinical data in eleven C3VD patients in pre and post-TAVR status. Our findings position this framework as a promising new non-invasive diagnostic tool that can provide blood flow metrics while posing no risk to the patient. The diagnostic information, that the framework can provide, is vitally needed to improve clinical outcomes, to assess patient risk and to plan treatment.
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Affiliation(s)
- Seyedvahid Khodaei
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada
| | - Alison Henstock
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada
| | - Reza Sadeghi
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada
| | - Stephanie Sellers
- grid.416553.00000 0000 8589 2327St. Paul’s Hospital, Vancouver, BC Canada ,grid.17091.3e0000 0001 2288 9830Department of Radiology, University of British Columbia, Vancouver, BC Canada
| | - Philipp Blanke
- grid.416553.00000 0000 8589 2327St. Paul’s Hospital, Vancouver, BC Canada ,grid.17091.3e0000 0001 2288 9830Department of Radiology, University of British Columbia, Vancouver, BC Canada
| | - Jonathon Leipsic
- grid.416553.00000 0000 8589 2327St. Paul’s Hospital, Vancouver, BC Canada ,grid.17091.3e0000 0001 2288 9830Department of Radiology, University of British Columbia, Vancouver, BC Canada
| | - Ali Emadi
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada ,grid.25073.330000 0004 1936 8227Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON Canada
| | - Zahra Keshavarz-Motamed
- grid.25073.330000 0004 1936 8227Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L7 Canada ,grid.25073.330000 0004 1936 8227School of Biomedical Engineering, McMaster University, Hamilton, ON Canada ,grid.25073.330000 0004 1936 8227School of Computational Science and Engineering, McMaster University, Hamilton, ON Canada
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16
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Niederer SA, Sacks MS, Girolami M, Willcox K. Scaling digital twins from the artisanal to the industrial. NATURE COMPUTATIONAL SCIENCE 2021; 1:313-320. [PMID: 38217216 DOI: 10.1038/s43588-021-00072-5] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/20/2021] [Indexed: 01/15/2024]
Abstract
Mathematical modeling and simulation are moving from being powerful development and analysis tools towards having increased roles in operational monitoring, control and decision support, in which models of specific entities are continually updated in the form of a digital twin. However, current digital twins are largely the result of bespoke technical solutions that are difficult to scale. We discuss two exemplar applications that motivate challenges and opportunities for scaling digital twins, and that underscore potential barriers to wider adoption of this technology.
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Affiliation(s)
- Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | - Michael S Sacks
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
| | - Mark Girolami
- Department of Engineering, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Karen Willcox
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA
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17
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Rodero C, Strocchi M, Marciniak M, Longobardi S, Whitaker J, O’Neill MD, Gillette K, Augustin C, Plank G, Vigmond EJ, Lamata P, Niederer SA. Linking statistical shape models and simulated function in the healthy adult human heart. PLoS Comput Biol 2021; 17:e1008851. [PMID: 33857152 PMCID: PMC8049237 DOI: 10.1371/journal.pcbi.1008851] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 03/03/2021] [Indexed: 01/09/2023] Open
Abstract
Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.
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Affiliation(s)
- Cristobal Rodero
- Cardiac Electromechanics Research Group, Biomedical Engineering Department, King´s College London, London, United Kingdom
- Cardiac Modelling and Imaging Biomarkers, Biomedical Engineering Department, King´s College London, London, United Kingdom
- * E-mail:
| | - Marina Strocchi
- Cardiac Electromechanics Research Group, Biomedical Engineering Department, King´s College London, London, United Kingdom
| | - Maciej Marciniak
- Cardiac Modelling and Imaging Biomarkers, Biomedical Engineering Department, King´s College London, London, United Kingdom
| | - Stefano Longobardi
- Cardiac Electromechanics Research Group, Biomedical Engineering Department, King´s College London, London, United Kingdom
| | - John Whitaker
- Cardiovascular Imaging Department, King’s College London, London, United Kingdom
| | - Mark D. O’Neill
- Department of Cardiology, St Thomas’ Hospital, London, United Kingdom
| | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | | | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Edward J. Vigmond
- Institute of Electrophysiology and Heart Modeling, Foundation Bordeaux University, Bordeaux, France
- Bordeaux Institute of Mathematics, University of Bordeaux, Bordeaux, France
| | - Pablo Lamata
- Cardiac Modelling and Imaging Biomarkers, Biomedical Engineering Department, King´s College London, London, United Kingdom
| | - Steven A. Niederer
- Cardiac Electromechanics Research Group, Biomedical Engineering Department, King´s College London, London, United Kingdom
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18
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Nguyen TD, Kadri OE, Voronov RS. An Introductory Overview of Image-Based Computational Modeling in Personalized Cardiovascular Medicine. Front Bioeng Biotechnol 2020; 8:529365. [PMID: 33102452 PMCID: PMC7546862 DOI: 10.3389/fbioe.2020.529365] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 08/31/2020] [Indexed: 02/05/2023] Open
Abstract
Cardiovascular diseases account for the number one cause of deaths in the world. Part of the reason for such grim statistics is our limited understanding of the underlying mechanisms causing these devastating pathologies, which is made difficult by the invasiveness of the procedures associated with their diagnosis (e.g., inserting catheters into the coronal artery to measure blood flow to the heart). Likewise, it is also difficult to design and test assistive devices without implanting them in vivo. However, with the recent advancements made in biomedical scanning technologies and computer simulations, image-based modeling (IBM) has arisen as the next logical step in the evolution of non-invasive patient-specific cardiovascular medicine. Yet, due to its novelty, it is still relatively unknown outside of the niche field. Therefore, the goal of this manuscript is to review the current state-of-the-art and the limitations of the methods used in this area of research, as well as their applications to personalized cardiovascular investigations and treatments. Specifically, the modeling of three different physics – electrophysiology, biomechanics and hemodynamics – used in the cardiovascular IBM is discussed in the context of the physiology that each one of them describes and the mechanisms of the underlying cardiac diseases that they can provide insight into. Only the “bare-bones” of the modeling approaches are discussed in order to make this introductory material more accessible to an outside observer. Additionally, the imaging methods, the aspects of the unique cardiac anatomy derived from them, and their relation to the modeling algorithms are reviewed. Finally, conclusions are drawn about the future evolution of these methods and their potential toward revolutionizing the non-invasive diagnosis, virtual design of treatments/assistive devices, and increasing our understanding of these lethal cardiovascular diseases.
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Affiliation(s)
- Thanh Danh Nguyen
- Otto H. York Department of Chemical and Materials Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Olufemi E Kadri
- Otto H. York Department of Chemical and Materials Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States.,UC-P&G Simulation Center, University of Cincinnati, Cincinnati, OH, United States
| | - Roman S Voronov
- Otto H. York Department of Chemical and Materials Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States.,Department of Biomedical Engineering, Newark College of Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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19
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Corrado C, Avezzù A, Lee AWC, Mendoca Costa C, Roney CH, Strocchi M, Bishop M, Niederer SA. Using cardiac ionic cell models to interpret clinical data. WIREs Mech Dis 2020; 13:e1508. [PMID: 33027553 DOI: 10.1002/wsbm.1508] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/27/2020] [Accepted: 09/04/2020] [Indexed: 01/24/2023]
Abstract
For over 100 years cardiac electrophysiology has been measured in the clinic. The electrical signals that can be measured span from noninvasive ECG and body surface potentials measurements through to detailed invasive measurements of local tissue electrophysiology. These electrophysiological measurements form a crucial component of patient diagnosis and monitoring; however, it remains challenging to quantitatively link changes in clinical electrophysiology measurements to biophysical cellular function. Multi-scale biophysical computational models represent one solution to this problem. These models provide a formal framework for linking cellular function through to emergent whole organ function and routine clinical diagnostic signals. In this review, we describe recent work on the use of computational models to interpret clinical electrophysiology signals. We review the simulation of human cardiac myocyte electrophysiology in the atria and the ventricles and how these models are being used to link organ scale function to patient disease mechanisms and therapy response in patients receiving implanted defibrillators, \cardiac resynchronisation therapy or suffering from atrial fibrillation and ventricular tachycardia. There is a growing use of multi-scale biophysical models to interpret clinical data. This allows cardiologists to link clinical observations with cellular mechanisms to better understand cardiopathophysiology and identify novel treatment strategies. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.
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20
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Strocchi M, Augustin CM, Gsell MAF, Karabelas E, Neic A, Gillette K, Razeghi O, Prassl AJ, Vigmond EJ, Behar JM, Gould J, Sidhu B, Rinaldi CA, Bishop MJ, Plank G, Niederer SA. A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations. PLoS One 2020; 15:e0235145. [PMID: 32589679 PMCID: PMC7319311 DOI: 10.1371/journal.pone.0235145] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/09/2020] [Indexed: 12/12/2022] Open
Abstract
Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. To facilitate the development of new simulation and model analysis techniques by groups without direct access to medical data, image analysis techniques and meshing tools, we have created the first publicly available virtual cohort of twenty-four four-chamber hearts. Our cohort was built from heart failure patients, age 67±14 years. We segmented four-chamber heart geometries from end-diastolic (ED) CT images and generated linear tetrahedral meshes with an average edge length of 1.1±0.2mm. Ventricular fibres were added in the ventricles with a rule-based method with an orientation of -60° and 80° at the epicardium and endocardium, respectively. We additionally refined the meshes to an average edge length of 0.39±0.10mm to show that all given meshes can be resampled to achieve an arbitrary desired resolution. We ran simulations for ventricular electrical activation and free mechanical contraction on all 1.1mm-resolution meshes to ensure that our meshes are suitable for electro-mechanical simulations. Simulations for electrical activation resulted in a total activation time of 149±16ms. Free mechanical contractions gave an average left ventricular (LV) and right ventricular (RV) ejection fraction (EF) of 35±1% and 30±2%, respectively, and a LV and RV stroke volume (SV) of 95±28mL and 65±11mL, respectively. By making the cohort publicly available, we hope to facilitate large cohort computational studies and to promote the development of cardiac computational electro-mechanics for clinical applications.
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Affiliation(s)
- Marina Strocchi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | | | | | - Elias Karabelas
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | | | - Karli Gillette
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | - Anton J. Prassl
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, F-33600 Pessac- Bordeaux, France
- University of Bordeaux, IMB, UMR 5251, F-33400 Talence, France
| | - Jonathan M. Behar
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Baldeep Sidhu
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Christopher A. Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, City of London, United Kingdom
| | - Martin J. Bishop
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Steiermark, Austria
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, City of London, United Kingdom
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21
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Niederer SA, Aboelkassem Y, Cantwell CD, Corrado C, Coveney S, Cherry EM, Delhaas T, Fenton FH, Panfilov AV, Pathmanathan P, Plank G, Riabiz M, Roney CH, dos Santos RW, Wang L. Creation and application of virtual patient cohorts of heart models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190558. [PMID: 32448064 PMCID: PMC7287335 DOI: 10.1098/rsta.2019.0558] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/06/2020] [Indexed: 05/21/2023]
Abstract
Patient-specific cardiac models are now being used to guide therapies. The increased use of patient-specific cardiac simulations in clinical care will give rise to the development of virtual cohorts of cardiac models. These cohorts will allow cardiac simulations to capture and quantify inter-patient variability. However, the development of virtual cohorts of cardiac models will require the transformation of cardiac modelling from small numbers of bespoke models to robust and rapid workflows that can create large numbers of models. In this review, we describe the state of the art in virtual cohorts of cardiac models, the process of creating virtual cohorts of cardiac models, and how to generate the individual cohort member models, followed by a discussion of the potential and future applications of virtual cohorts of cardiac models. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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Affiliation(s)
| | | | | | | | | | - E. M. Cherry
- Georgia Institute of Technology, Atlanta, GA, USA
| | - T. Delhaas
- Maastricht University, Maastricht, the Netherlands
| | - F. H. Fenton
- Georgia Institute of Technology, Atlanta, GA, USA
| | - A. V. Panfilov
- Ghent University, Gent, Belgium
- Laboratory of Computational Biology and Medicine, Ural Federal University, Ekaterinburg, Russia
| | - P. Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Administration, Rockville, MD, USA
| | - G. Plank
- Medical University of Graz, Graz, Austria
| | | | | | | | - L. Wang
- Rochester Institute of Technology, La JollaRochester, NY, USA
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22
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23
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Strocchi M, Gsell MAF, Augustin CM, Razeghi O, Roney CH, Prassl AJ, Vigmond EJ, Behar JM, Gould JS, Rinaldi CA, Bishop MJ, Plank G, Niederer SA. Simulating ventricular systolic motion in a four-chamber heart model with spatially varying robin boundary conditions to model the effect of the pericardium. J Biomech 2020; 101:109645. [PMID: 32014305 PMCID: PMC7677892 DOI: 10.1016/j.jbiomech.2020.109645] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 01/15/2020] [Accepted: 01/15/2020] [Indexed: 12/11/2022]
Abstract
The pericardium affects cardiac motion by limiting epicardial displacement normal to the surface. In computational studies, it is important for the model to replicate realistic motion, as this affects the physiological fidelity of the model. Previous computational studies showed that accounting for the effect of the pericardium allows for a more realistic motion simulation. In this study, we describe the mechanism through which the pericardium causes improved cardiac motion. We simulated electrical activation and contraction of the ventricles on a four-chamber heart in the presence and absence of the effect of the pericardium. We simulated the mechanical constraints imposed by the pericardium by applying normal Robin boundary conditions on the ventricular epicardium. We defined a regional scaling of normal springs stiffness based on image-derived motion from CT images. The presence of the pericardium reduced the error between simulated and image-derived end-systolic configurations from 12.8±4.1 mm to 5.7±2.5 mm. First, the pericardium prevents the ventricles from spherising during isovolumic contraction, reducing the outward motion of the free walls normal to the surface and the upwards motion of the apex. Second, by restricting the inward motion of the free and apical walls of the ventricles the pericardium increases atrioventricular plane displacement by four folds during ejection. Our results provide a mechanistic explanation of the importance of the pericardium in physiological simulations of electromechanical cardiac function.
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Affiliation(s)
- Marina Strocchi
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | | | | | - Orod Razeghi
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Caroline H Roney
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Anton J Prassl
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - Edward J Vigmond
- University of Bordeaux, Talence, France; LIRYC Electrophysiology and Heart Modeling Institute, Campus Xavier Arnozan, Pessac, France
| | - Jonathan M Behar
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Cardiology Department, Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Justin S Gould
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Cardiology Department, Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Christopher A Rinaldi
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Cardiology Department, Guys and St Thomas' NHS Foundation Trust, London, UK
| | - Martin J Bishop
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Gernot Plank
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - Steven A Niederer
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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Abstract
The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.
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25
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Di Achille P, Parikh J, Khamzin S, Solovyova O, Kozloski J, Gurev V. Model order reduction for left ventricular mechanics via congruency training. PLoS One 2020; 15:e0219876. [PMID: 31905197 PMCID: PMC6944464 DOI: 10.1371/journal.pone.0219876] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 12/10/2019] [Indexed: 12/20/2022] Open
Abstract
Computational models of the cardiovascular system and specifically heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. To achieve clinical utility, models should be able to assimilate the diagnostic multi-modality data available for each patient and generate consistent representations of the underlying cardiovascular physiology. While finite element models of the heart can naturally account for patient-specific anatomies reconstructed from medical images, optimizing the many other parameters driving simulated cardiac functions is challenging due to computational complexity. With the goal of streamlining parameter adaptation, in this paper we present a novel, multifidelity strategy for model order reduction of 3-D finite element models of ventricular mechanics. Our approach is centered around well established findings on the similarity between contraction of an isolated muscle and the whole ventricle. Specifically, we demonstrate that simple linear transformations between sarcomere strain (tension) and ventricular volume (pressure) are sufficient to reproduce global pressure-volume outputs of 3-D finite element models even by a reduced model with just a single myocyte unit. We further develop a procedure for congruency training of a surrogate low-order model from multi-scale finite elements, and we construct an example of parameter optimization based on medical images. We discuss how the presented approach might be employed to process large datasets of medical images as well as databases of echocardiographic reports, paving the way towards application of heart mechanics models in the clinical practice.
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Affiliation(s)
- Paolo Di Achille
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States of America
| | - Jaimit Parikh
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States of America
| | - Svyatoslav Khamzin
- Ural Federal University, Yekaterinburg, Russia
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences (UB RAS), Yekaterinburg, Russia
| | - Olga Solovyova
- Ural Federal University, Yekaterinburg, Russia
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences (UB RAS), Yekaterinburg, Russia
| | - James Kozloski
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States of America
| | - Viatcheslav Gurev
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States of America
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26
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Lee AWC, Nguyen UC, Razeghi O, Gould J, Sidhu BS, Sieniewicz B, Behar J, Mafi-Rad M, Plank G, Prinzen FW, Rinaldi CA, Vernooy K, Niederer S. A rule-based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non-invasive clinical data. Med Image Anal 2019; 57:197-213. [PMID: 31326854 PMCID: PMC6746621 DOI: 10.1016/j.media.2019.06.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 06/20/2019] [Accepted: 06/27/2019] [Indexed: 12/13/2022]
Abstract
Background Cardiac Resynchronization Therapy (CRT) is one of the few effective treatments for heart failure patients with ventricular dyssynchrony. The pacing location of the left ventricle is indicated as a determinant of CRT outcome. Objective Patient specific computational models allow the activation pattern following CRT implant to be predicted and this may be used to optimize CRT lead placement. Methods In this study, the effects of heterogeneous cardiac substrate (scar, fast endocardial conduction, slow septal conduction, functional block) on accurately predicting the electrical activation of the LV epicardium were tested to determine the minimal detail required to create a rule based model of cardiac electrophysiology. Non-invasive clinical data (CT or CMR images and 12 lead ECG) from eighteen patients from two centers were used to investigate the models. Results Validation with invasive electro-anatomical mapping data identified that computer models with fast endocardial conduction were able to predict the electrical activation with a mean distance errors of 9.2 ± 0.5 mm (CMR data) or (CT data) 7.5 ± 0.7 mm. Conclusion This study identified a simple rule-based fast endocardial conduction model, built using non-invasive clinical data that can be used to rapidly and robustly predict the electrical activation of the heart. Pre-procedural prediction of the latest electrically activating region to identify the optimal LV pacing site could potentially be a useful clinical planning tool for CRT procedures.
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Affiliation(s)
- A W C Lee
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - U C Nguyen
- Department of Physiology, Maastricht University Medical Center (MUMC+), Cardiovascular Research Institute Maastricht (CARIM), Maastricht, the Netherlands; Department of Cardiology, Maastricht University Medical Center (MUMC+), Cardiovascular Research Institute Maastricht (CARIM), Maastricht, the Netherlands
| | - O Razeghi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - J Gould
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - B S Sidhu
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - B Sieniewicz
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - J Behar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Bart's Heart Centre, St. Bartholomew's Hospital, London, United Kingdom
| | - M Mafi-Rad
- Department of Cardiology, Maastricht University Medical Center (MUMC+), Cardiovascular Research Institute Maastricht (CARIM), Maastricht, the Netherlands
| | - G Plank
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - F W Prinzen
- Department of Physiology, Maastricht University Medical Center (MUMC+), Cardiovascular Research Institute Maastricht (CARIM), Maastricht, the Netherlands
| | - C A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - K Vernooy
- Department of Cardiology, Maastricht University Medical Center (MUMC+), Cardiovascular Research Institute Maastricht (CARIM), Maastricht, the Netherlands; Department of Cardiology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - S Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Seetharam K, Shrestha S, Sengupta PP. Artificial Intelligence in Cardiovascular Medicine. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2019; 21:25. [PMID: 31089906 PMCID: PMC7561035 DOI: 10.1007/s11936-019-0728-1] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
PURPOSE OF REVIEW The ripples of artificial intelligence are being felt in various sectors of human life. Machine learning, a subset of artificial intelligence, extracts information from large databases of information and is gaining traction in various fields of cardiology. In this review, we highlight noteworthy examples of machine learning utilization in echocardiography, nuclear cardiology, computed tomography, and magnetic resonance imaging over the past year. RECENT FINDINGS In the past year, machine learning (ML) has expanded its boundaries in cardiology with several positive results. Some studies have integrated clinical and imaging information to further augment the accuracy of these ML algorithms. All the studies mentioned in this review have clearly demonstrated superior results of ML in relation to conventional approaches for identifying obstructions or predicting major adverse events in reference to conventional approaches. As the influx of data arriving from gradually evolving technologies in health care and wearable devices continues to be more complex, ML may serve as the bridge to transcend the gap between health care and patients in the future. In order to facilitate a seamless transition between both, a few issues must be resolved for a successful implementation of ML in health care.
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Affiliation(s)
- Karthik Seetharam
- WVU Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Sirish Shrestha
- WVU Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA
| | - Partho P Sengupta
- WVU Heart & Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA.
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28
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Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, Marwick TH. Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. J Am Coll Cardiol 2019; 73:1317-1335. [PMID: 30898208 PMCID: PMC6474254 DOI: 10.1016/j.jacc.2018.12.054] [Citation(s) in RCA: 303] [Impact Index Per Article: 60.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 12/13/2018] [Indexed: 12/11/2022]
Abstract
Data science is likely to lead to major changes in cardiovascular imaging. Problems with timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The application of artificial intelligence (AI) is dependent on robust data; the application of appropriate computational approaches and tools; and validation of its clinical application to image segmentation, automated measurements, and eventually, automated diagnosis. AI may reduce cost and improve value at the stages of image acquisition, interpretation, and decision-making. Moreover, the precision now possible with cardiovascular imaging, combined with "big data" from the electronic health record and pathology, is likely to better characterize disease and personalize therapy. This review summarizes recent promising applications of AI in cardiology and cardiac imaging, which potentially add value to patient care.
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Affiliation(s)
- Damini Dey
- Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, California
| | - Piotr J Slomka
- Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, California
| | - Paul Leeson
- Oxford Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Sirish Shrestha
- Section of Cardiology, West Virginia University, Morgantown, West Virginia
| | - Partho P Sengupta
- Section of Cardiology, West Virginia University, Morgantown, West Virginia
| | - Thomas H Marwick
- Baker Heart and Diabetes Research Institute, Melbourne, Australia.
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29
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Molléro R, Pennec X, Delingette H, Ayache N, Sermesant M. Population-based priors in cardiac model personalisation for consistent parameter estimation in heterogeneous databases. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3158. [PMID: 30239175 DOI: 10.1002/cnm.3158] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 09/10/2018] [Accepted: 09/16/2018] [Indexed: 06/08/2023]
Abstract
Personalised cardiac models are a virtual representation of the patient heart, with parameter values for which the simulation fits the available clinical measurements. Models usually have a large number of parameters while the available data for a given patient are typically limited to a small set of measurements; thus, the parameters cannot be estimated uniquely. This is a practical obstacle for clinical applications, where accurate parameter values can be important. Here, we explore an original approach based on an algorithm called Iteratively Updated Priors (IUP), in which we perform successive personalisations of a full database through maximum a posteriori (MAP) estimation, where the prior probability at an iteration is set from the distribution of personalised parameters in the database at the previous iteration. At the convergence of the algorithm, estimated parameters of the population lie on a linear subspace of reduced (and possibly sufficient) dimension in which for each case of the database, there is a (possibly unique) parameter value for which the simulation fits the measurements. We first show how this property can help the modeller select a relevant parameter subspace for personalisation. In addition, since the resulting priors in this subspace represent the population statistics in this subspace, they can be used to perform consistent parameter estimation for cases where measurements are possibly different or missing in the database, which we illustrate with the personalisation of a heterogeneous database of 811 cases.
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Affiliation(s)
- Roch Molléro
- Inria, Epione Research Project, Sophia Antipolis, France
| | - Xavier Pennec
- Inria, Epione Research Project, Sophia Antipolis, France
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30
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Personalised deposition maps for micro- and nanoparticles targeting an atherosclerotic plaque: attributions to the receptor-mediated adsorption on the inflamed endothelial cells. Biomech Model Mechanobiol 2019; 18:813-828. [DOI: 10.1007/s10237-018-01116-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2018] [Accepted: 12/29/2018] [Indexed: 01/25/2023]
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31
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Niederer SA, Campbell KS, Campbell SG. A short history of the development of mathematical models of cardiac mechanics. J Mol Cell Cardiol 2018; 127:11-19. [PMID: 30503754 PMCID: PMC6525149 DOI: 10.1016/j.yjmcc.2018.11.015] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 11/02/2018] [Accepted: 11/21/2018] [Indexed: 11/15/2022]
Abstract
Cardiac mechanics plays a crucial role in atrial and ventricular function, in the regulation of growth and remodelling, in the progression of disease, and the response to treatment. The spatial scale of the critical mechanisms ranges from nm (molecules) to cm (hearts) with the fastest events occurring in milliseconds (molecular events) and the slowest requiring months (growth and remodelling). Due to its complexity and importance, cardiac mechanics has been studied extensively both experimentally and through mathematical models and simulation. Models of cardiac mechanics evolved from seminal studies in skeletal muscle, and developed into cardiac specific, species specific, human specific and finally patient specific calculations. These models provide a formal framework to link multiple experimental assays recorded over nearly 100 years into a single unified representation of cardiac function. This review first provides a summary of the proteins, physiology and anatomy involved in the generation of cardiac pump function. We then describe the evolution of models of cardiac mechanics starting with the early theoretical frameworks describing the link between sarcomeres and muscle contraction, transitioning through myosin-level models to calcium-driven systems, and ending with whole heart patient-specific models.
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Affiliation(s)
| | - Kenneth S Campbell
- Department of Physiology and Division of Cardiovascular Medicine, University of Kentucky, Lexington, USA
| | - Stuart G Campbell
- Departments of Biomedical Engineering and Cellular and Molecular Physiology, Yale University, New Haven, USA
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32
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Abstract
Computational cardiology is the scientific field devoted to the development of methodologies that enhance our mechanistic understanding, diagnosis and treatment of cardiovascular disease. In this regard, the field embraces the extraordinary pace of discovery in imaging, computational modeling, and cardiovascular informatics at the intersection of atherogenesis and vascular biology. This paper highlights existing methods, practices, and computational models and proposes new strategies to support a multidisciplinary effort in this space. We focus on the means by that to leverage and coalesce these multiple disciplines to advance translational science and computational cardiology. Analyzing the scientific trends and understanding the current needs we present our perspective for the future of cardiovascular treatment.
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33
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Abstract
As biologists discover and learn to embrace the complexity of biological systems, computational data analysis and modeling have become critical for furthering our understanding. Exascale computing will enable the development of new predictive multiscale models, transforming how we study the behaviors of organisms and ecosystems, ultimately leading to new innovations and discoveries.
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34
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Lee AWC, Costa CM, Strocchi M, Rinaldi CA, Niederer SA. Computational Modeling for Cardiac Resynchronization Therapy. J Cardiovasc Transl Res 2018; 11:92-108. [PMID: 29327314 PMCID: PMC5908824 DOI: 10.1007/s12265-017-9779-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 12/18/2017] [Indexed: 11/21/2022]
Abstract
Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure (HF) patients with an electrical substrate pathology causing ventricular dyssynchrony. However 40-50% of patients do not respond to treatment. Cardiac modeling of the electrophysiology, electromechanics, and hemodynamics of the heart has been used to study mechanisms behind HF pathology and CRT response. Recently, multi-scale dyssynchronous HF models have been used to study optimal device settings and optimal lead locations, investigate the underlying cardiac pathophysiology, as well as investigate emerging technologies proposed to treat cardiac dyssynchrony. However the breadth of patient and experimental data required to create and parameterize these models and the computational resources required currently limits the use of these models to small patient numbers. In the future, once these technical challenges are overcome, biophysically based models of the heart have the potential to become a clinical tool to aid in the diagnosis and treatment of HF.
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Affiliation(s)
- Angela W C Lee
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | | | - Marina Strocchi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | | | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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35
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Gray RA, Pathmanathan P. Patient-Specific Cardiovascular Computational Modeling: Diversity of Personalization and Challenges. J Cardiovasc Transl Res 2018; 11:80-88. [PMID: 29512059 PMCID: PMC5908828 DOI: 10.1007/s12265-018-9792-2] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 02/02/2018] [Indexed: 02/07/2023]
Abstract
Patient-specific computer models have been developed representing a variety of aspects of the cardiovascular system spanning the disciplines of electrophysiology, electromechanics, solid mechanics, and fluid dynamics. These physiological mechanistic models predict macroscopic phenomena such as electrical impulse propagation and contraction throughout the entire heart as well as flow and pressure dynamics occurring in the ventricular chambers, aorta, and coronary arteries during each heartbeat. Such models have been used to study a variety of clinical scenarios including aortic aneurysms, coronary stenosis, cardiac valvular disease, left ventricular assist devices, cardiac resynchronization therapy, ablation therapy, and risk stratification. After decades of research, these models are beginning to be incorporated into clinical practice directly via marketed devices and indirectly by improving our understanding of the underlying mechanisms of health and disease within a clinical context.
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Affiliation(s)
- Richard A Gray
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA.
- , Silver Spring, USA.
| | - Pras Pathmanathan
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, 20993, USA
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36
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37
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Christ B, Dahmen U, Herrmann KH, König M, Reichenbach JR, Ricken T, Schleicher J, Ole Schwen L, Vlaic S, Waschinsky N. Computational Modeling in Liver Surgery. Front Physiol 2017; 8:906. [PMID: 29249974 PMCID: PMC5715340 DOI: 10.3389/fphys.2017.00906] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 10/25/2017] [Indexed: 12/13/2022] Open
Abstract
The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.
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Affiliation(s)
- Bruno Christ
- Molecular Hepatology Lab, Clinics of Visceral, Transplantation, Thoracic and Vascular Surgery, University Hospital Leipzig, University of Leipzig, Leipzig, Germany
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Matthias König
- Department of Biology, Institute for Theoretical Biology, Humboldt University of Berlin, Berlin, Germany
| | - Jürgen R Reichenbach
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Friedrich Schiller University Jena, Jena, Germany
| | - Tim Ricken
- Mechanics, Structural Analysis, and Dynamics, TU Dortmund University, Dortmund, Germany
| | - Jana Schleicher
- Experimental Transplantation Surgery, Department of General, Visceral and Vascular Surgery, University Hospital Jena, Jena, Germany.,Department of Bioinformatics, Friedrich Schiller University Jena, Jena, Germany
| | | | - Sebastian Vlaic
- Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Jena, Germany
| | - Navina Waschinsky
- Mechanics, Structural Analysis, and Dynamics, TU Dortmund University, Dortmund, Germany
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Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models. Biomech Model Mechanobiol 2017; 17:285-300. [PMID: 28894984 DOI: 10.1007/s10237-017-0960-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Accepted: 08/31/2017] [Indexed: 10/18/2022]
Abstract
Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified "0D" version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.
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39
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Niederer SA, Smith NP. Using physiologically based models for clinical translation: predictive modelling, data interpretation or something in-between? J Physiol 2016; 594:6849-6863. [PMID: 27121495 PMCID: PMC5134392 DOI: 10.1113/jp272003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Accepted: 03/13/2016] [Indexed: 02/02/2023] Open
Abstract
Heart disease continues to be a significant clinical problem in Western society. Predictive models and simulations that integrate physiological understanding with patient information derived from clinical data have huge potential to contribute to improving our understanding of both the progression and treatment of heart disease. In particular they provide the potential to improve patient selection and optimisation of cardiovascular interventions across a range of pathologies. Currently a significant proportion of this potential is still to be realised. In this paper we discuss the opportunities and challenges associated with this realisation. Reviewing the successful elements of model translation for biophysically based models and the emerging supporting technologies, we propose three distinct modes of clinical translation. Finally we outline the challenges ahead that will be fundamental to overcome if the ultimate goal of fully personalised clinical cardiac care is to be achieved.
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Affiliation(s)
- Steven A. Niederer
- Department of Biomedical Engineering and Imaging SciencesSt Thomas’ HospitalKing's College LondonThe Rayne Institute4th Floor Lambeth WingLondonSE1 7EHUK
| | - Nic P. Smith
- Department of Biomedical Engineering and Imaging SciencesSt Thomas’ HospitalKing's College LondonThe Rayne Institute4th Floor Lambeth WingLondonSE1 7EHUK
- Engineering School Block 1University of AucklandLevel 5, 20 Symonds StreetAuckland101New Zealand
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Aznar JMG, Valero C, Borau C, Garijo N. Computational mechano-chemo-biology: a tool for the design of tissue scaffolds. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/s40898-016-0002-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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41
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Amr A, Kayvanpour E, Sedaghat-Hamedani F, Passerini T, Mihalef V, Lai A, Neumann D, Georgescu B, Buss S, Mereles D, Zitron E, Posch AE, Würstle M, Mansi T, Katus HA, Meder B. Personalized Computer Simulation of Diastolic Function in Heart Failure. GENOMICS PROTEOMICS & BIOINFORMATICS 2016; 14:244-52. [PMID: 27477449 PMCID: PMC4996856 DOI: 10.1016/j.gpb.2016.04.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 04/21/2016] [Accepted: 04/26/2016] [Indexed: 01/14/2023]
Abstract
The search for a parameter representing left ventricular relaxation from non-invasive and invasive diagnostic tools has been extensive, since heart failure (HF) with preserved ejection fraction (HF-pEF) is a global health problem. We explore here the feasibility using patient-specific cardiac computer modeling to capture diastolic parameters in patients suffering from different degrees of systolic HF. Fifty eight patients with idiopathic dilated cardiomyopathy have undergone thorough clinical evaluation, including cardiac magnetic resonance imaging (MRI), heart catheterization, echocardiography, and cardiac biomarker assessment. A previously-introduced framework for creating multi-scale patient-specific cardiac models has been applied on all these patients. Novel parameters, such as global stiffness factor and maximum left ventricular active stress, representing cardiac active and passive tissue properties have been computed for all patients. Invasive pressure measurements from heart catheterization were then used to evaluate ventricular relaxation using the time constant of isovolumic relaxation Tau (τ). Parameters from heart catheterization and the multi-scale model have been evaluated and compared to patient clinical presentation. The model parameter global stiffness factor, representing diastolic passive tissue properties, is correlated significantly across the patient population with τ. This study shows that multi-modal cardiac models can successfully capture diastolic (dys) function, a prerequisite for future clinical trials on HF-pEF.
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Affiliation(s)
- Ali Amr
- Institute for Cardiomyopathies, Department of Medicine III, University of Heidelberg, 69120 Heidelberg, Germany; German Centre for Cardiovascular Research (DZHK), Heidelberg/Mannheim, Germany
| | - Elham Kayvanpour
- Institute for Cardiomyopathies, Department of Medicine III, University of Heidelberg, 69120 Heidelberg, Germany; German Centre for Cardiovascular Research (DZHK), Heidelberg/Mannheim, Germany
| | - Farbod Sedaghat-Hamedani
- Institute for Cardiomyopathies, Department of Medicine III, University of Heidelberg, 69120 Heidelberg, Germany; German Centre for Cardiovascular Research (DZHK), Heidelberg/Mannheim, Germany
| | - Tiziano Passerini
- Siemens Healthcare, Medical Imaging Technologies, Princeton, NJ 08540, USA
| | - Viorel Mihalef
- Siemens Healthcare, Medical Imaging Technologies, Princeton, NJ 08540, USA
| | - Alan Lai
- Institute for Cardiomyopathies, Department of Medicine III, University of Heidelberg, 69120 Heidelberg, Germany
| | - Dominik Neumann
- Siemens Healthcare, Medical Imaging Technologies, Princeton, NJ 08540, USA
| | - Bogdan Georgescu
- Siemens Healthcare, Medical Imaging Technologies, Princeton, NJ 08540, USA
| | - Sebastian Buss
- Institute for Cardiomyopathies, Department of Medicine III, University of Heidelberg, 69120 Heidelberg, Germany
| | - Derliz Mereles
- Institute for Cardiomyopathies, Department of Medicine III, University of Heidelberg, 69120 Heidelberg, Germany
| | - Edgar Zitron
- Institute for Cardiomyopathies, Department of Medicine III, University of Heidelberg, 69120 Heidelberg, Germany
| | - Andreas E Posch
- Siemens Healthcare, Strategy and Innovation, 91052 Erlangen, Germany
| | | | - Tommaso Mansi
- Siemens Healthcare, Medical Imaging Technologies, Princeton, NJ 08540, USA
| | - Hugo A Katus
- Institute for Cardiomyopathies, Department of Medicine III, University of Heidelberg, 69120 Heidelberg, Germany; German Centre for Cardiovascular Research (DZHK), Heidelberg/Mannheim, Germany
| | - Benjamin Meder
- Institute for Cardiomyopathies, Department of Medicine III, University of Heidelberg, 69120 Heidelberg, Germany; German Centre for Cardiovascular Research (DZHK), Heidelberg/Mannheim, Germany.
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Comaniciu D, Engel K, Georgescu B, Mansi T. Shaping the future through innovations: From medical imaging to precision medicine. Med Image Anal 2016; 33:19-26. [PMID: 27349829 DOI: 10.1016/j.media.2016.06.016] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Revised: 06/08/2016] [Accepted: 06/13/2016] [Indexed: 10/21/2022]
Abstract
Medical images constitute a source of information essential for disease diagnosis, treatment and follow-up. In addition, due to its patient-specific nature, imaging information represents a critical component required for advancing precision medicine into clinical practice. This manuscript describes recently developed technologies for better handling of image information: photorealistic visualization of medical images with Cinematic Rendering, artificial agents for in-depth image understanding, support for minimally invasive procedures, and patient-specific computational models with enhanced predictive power. Throughout the manuscript we will analyze the capabilities of such technologies and extrapolate on their potential impact to advance the quality of medical care, while reducing its cost.
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Affiliation(s)
- Dorin Comaniciu
- Medical Imaging Technologies, Siemens Healthcare Technology Center, Princeton, NJ, USA
| | - Klaus Engel
- Medical Imaging Technologies, Siemens Healthcare Technology Center, Erlangen, Germany
| | - Bogdan Georgescu
- Medical Imaging Technologies, Siemens Healthcare Technology Center, Princeton, NJ, USA
| | - Tommaso Mansi
- Medical Imaging Technologies, Siemens Healthcare Technology Center, Princeton, NJ, USA.
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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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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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Panthee N, Okada JI, Washio T, Mochizuki Y, Suzuki R, Koyama H, Ono M, Hisada T, Sugiura S. Tailor-made heart simulation predicts the effect of cardiac resynchronization therapy in a canine model of heart failure. Med Image Anal 2016; 31:46-62. [PMID: 26973218 DOI: 10.1016/j.media.2016.02.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2015] [Revised: 02/12/2016] [Accepted: 02/15/2016] [Indexed: 11/25/2022]
Abstract
Despite extensive studies on clinical indices for the selection of patient candidates for cardiac resynchronization therapy (CRT), approximately 30% of selected patients do not respond to this therapy. Herein, we examined whether CRT simulations based on individualized realistic three-dimensional heart models can predict the therapeutic effect of CRT in a canine model of heart failure with left bundle branch block. In four canine models of failing heart with dyssynchrony, individualized three-dimensional heart models reproducing the electromechanical activity of each animal were created based on the computer tomographic images. CRT simulations were performed for 25 patterns of three ventricular pacing lead positions. Lead positions producing the best and the worst therapeutic effects were selected in each model. The validity of predictions was tested in acute experiments in which hearts were paced from the sites identified by simulations. We found significant correlations between the experimentally observed improvement in ejection fraction (EF) and the predicted improvements in ejection fraction (P<0.01) or the maximum value of the derivative of left ventricular pressure (P<0.01). The optimal lead positions produced better outcomes compared with the worst positioning in all dogs studied, although there were significant variations in responses. Variations in ventricular wall thickness among the dogs may have contributed to these responses. Thus CRT simulations using the individualized three-dimensional heart models can predict acute hemodynamic improvement, and help determine the optimal positions of the pacing lead.
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Affiliation(s)
- Nirmal Panthee
- Department of Cardiac Surgery, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655 Japan
| | - Jun-ichi Okada
- Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 178-4-4 Wakashiba, Kashiwa, Chiba, 277-0871 Japan; UT-Heart Inc. 3-25-8 Nozawa, Setagaya-ku, Tokyo 154-0003 Japan
| | - Takumi Washio
- Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 178-4-4 Wakashiba, Kashiwa, Chiba, 277-0871 Japan; UT-Heart Inc. 3-25-8 Nozawa, Setagaya-ku, Tokyo 154-0003 Japan
| | - Youhei Mochizuki
- Laboratory of Veterinary Internal Medicine, Nippon Veterinary and Life Science University, 1-7-1 Kyonancho, Musashino-shi, Tokyo 180-8602 Japan
| | - Ryohei Suzuki
- Laboratory of Veterinary Internal Medicine, Nippon Veterinary and Life Science University, 1-7-1 Kyonancho, Musashino-shi, Tokyo 180-8602 Japan
| | - Hidekazu Koyama
- Laboratory of Veterinary Internal Medicine, Nippon Veterinary and Life Science University, 1-7-1 Kyonancho, Musashino-shi, Tokyo 180-8602 Japan
| | - Minoru Ono
- Department of Cardiac Surgery, School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655 Japan
| | - Toshiaki Hisada
- Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 178-4-4 Wakashiba, Kashiwa, Chiba, 277-0871 Japan; UT-Heart Inc. 3-25-8 Nozawa, Setagaya-ku, Tokyo 154-0003 Japan
| | - Seiryo Sugiura
- Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, 178-4-4 Wakashiba, Kashiwa, Chiba, 277-0871 Japan; UT-Heart Inc. 3-25-8 Nozawa, Setagaya-ku, Tokyo 154-0003 Japan.
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Ovcharenko E, Klyshnikov K, Yuzhalin A, Savrasov G, Kokov A, Batranin A, Ganyukov V, Kudryavtseva Y. Modeling of transcatheter aortic valve replacement: Patient specific vs general approaches based on finite element analysis. Comput Biol Med 2016; 69:29-36. [DOI: 10.1016/j.compbiomed.2015.12.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Revised: 11/20/2015] [Accepted: 12/02/2015] [Indexed: 11/26/2022]
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