1
|
Strocchi M, Rodero C, Roney CH, Mendonca Costa C, Plank G, Lamata P, Niederer SA. A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes. Methods Mol Biol 2024; 2735:117-127. [PMID: 38038846 DOI: 10.1007/978-1-0716-3527-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2023]
Abstract
Computational models for cardiac electro-mechanics have been increasingly used to further understand heart function. Small cohort and single patient computational studies provide useful insight into cardiac pathophysiology and response to therapy. However, these smaller studies have limited capability to capture the high level of anatomical variability seen in cardiology patients. Larger cohort studies are, on the other hand, more representative of the study population, but building several patient-specific anatomical meshes can be time-consuming and requires access to larger datasets of imaging data, image processing software to label anatomical structures and tools to create high fidelity anatomical meshes. Limited access to these tools and data might limit advances in this area of research. In this chapter, we present our semi-automatic pipeline to build patient-specific four-chamber heart meshes from CT imaging datasets, including ventricular myofibers and a set of universal ventricular and atrial coordinates. This pipeline was applied to CT images from both heart failure patients and healthy controls to generate cohorts of tetrahedral meshes suitable for electro-mechanics simulations. Both cohorts were made publicly available in order to promote computational studies employing large virtual cohorts.
Collapse
Affiliation(s)
- Marina Strocchi
- Department of Biomedical Engineering, King's College London, London, UK
| | - Cristobal Rodero
- Department of Biomedical Engineering, King's College London, London, UK
| | - Caroline H Roney
- Department of Biomedical Engineering, King's College London, London, UK
| | | | | | - Pablo Lamata
- Department of Biomedical Engineering, King's College London, London, UK
| | - Steven A Niederer
- Department of Biomedical Engineering, King's College London, London, UK.
| |
Collapse
|
2
|
Yogev D, Tejman-Yarden S, Feinberg O, Parmet Y, Goldberg T, Illouz S, Nagar N, Freidin D, Vazgovsky O, Chatterji S, Salem Y, Katz U, Goitein O. Proof of concept: Comparative accuracy of semiautomated VR modeling for volumetric analysis of the heart ventricles. Heliyon 2022; 8:e11250. [PMID: 36387466 PMCID: PMC9641195 DOI: 10.1016/j.heliyon.2022.e11250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 10/12/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022] Open
Abstract
Introduction Simpson's rule is generally used to estimate cardiac volumes. By contrast, modern methods such as Virtual Reality (VR) utilize mesh modeling to present the object's surface spatial structure, thus enabling intricate volumetric calculations. In this study, two types of semiautomated VR models for cardiac volumetric analysis were compared to the standard Philips dedicated cardiac imaging platform (PDP) which is based on Simpson's rule calculations. Methods This retrospective report examined the cardiac computed tomography angiography (CCTA) of twenty patients with atrial fibrillation obtained prior to a left atrial appendage occlusion procedure. We employed two VR models to evaluate each CCTA and compared them to the PDP: a VR model with Philips-similar segmentations (VR-PS) that included the trabeculae and the papillary muscles within the luminal volume, and a VR model that only included the inner blood pool (VR-IBP). Results Comparison of the VR-PS and the PDP left ventricle (LV) volumes demonstrated excellent correlation with a ρc of 0.983 (95% CI 0.96, 0.99), and a small mean difference and range. The calculated volumes of the right ventricle (RV) had a somewhat lower correlation of 0.89 (95% CI 0.781, 0.95), a small mean difference, and a broader range. The VR-IBP chamber size estimations were significantly smaller than the estimates based on the PDP. Discussion Simpson's rule and polygon summation algorithms produce similar results in normal morphological LVs. However, this correlation failed to emerge when applied to RVs and irregular chambers. Conclusions The findings suggest that the polygon summation method is preferable for RV and irregular LV volume and function calculations.
Collapse
Affiliation(s)
- David Yogev
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- The Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel
| | - Shai Tejman-Yarden
- The Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel
- The Edmond J. Safra International Congenital Heart Center, Sheba Medical Center, Ramat Gan, Israel
- Corresponding author.
| | - Omer Feinberg
- The Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel
| | - Yisrael Parmet
- Department of Industrial Engineering and Management, Ben Gurion University, Beer Sheva, Israel
| | - Tomer Goldberg
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Shay Illouz
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- The Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel
| | - Netanel Nagar
- The Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel
- Industrial Design Department, Bezalel Academy of Art and Design, Jerusalem, Israel
| | - Dor Freidin
- The Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- The Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel
| | - Oliana Vazgovsky
- The Engineering Medical Research Lab, Sheba Medical Center, Ramat Gan, Israel
- The Edmond J. Safra International Congenital Heart Center, Sheba Medical Center, Ramat Gan, Israel
| | - Sumit Chatterji
- The Pulmonology Unit, Sheba Medical Center, Ramat Gan, Israel
- Interventional Pulmonology Unit, Sheba Medical Center, Ramat Gan, Israel
| | - Yishay Salem
- The Edmond J. Safra International Congenital Heart Center, Sheba Medical Center, Ramat Gan, Israel
- The Leviev Heart Institute, Sheba Medical Center, Ramat Gan, Israel
| | - Uriel Katz
- The Edmond J. Safra International Congenital Heart Center, Sheba Medical Center, Ramat Gan, Israel
- The Leviev Heart Institute, Sheba Medical Center, Ramat Gan, Israel
| | - Orly Goitein
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel
| |
Collapse
|
3
|
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.
Collapse
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:
| |
Collapse
|
4
|
Campos FO, Neic A, Mendonca Costa C, Whitaker J, O'Neill M, Razavi R, Rinaldi CA, DanielScherr, Niederer SA, Plank G, Bishop MJ. An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias. Med Image Anal 2022; 80:102483. [PMID: 35667328 PMCID: PMC10114098 DOI: 10.1016/j.media.2022.102483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 04/22/2022] [Accepted: 05/20/2022] [Indexed: 02/05/2023]
Abstract
Catheter ablation is currently the only curative treatment for scar-related ventricular tachycardias (VTs). However, not only are ablation procedures long, with relatively high risk, but success rates are punitively low, with frequent VT recurrence. Personalized in-silico approaches have the opportunity to address these limitations. However, state-of-the-art reaction diffusion (R-D) simulations of VT induction and subsequent circuits used for in-silico ablation target identification require long execution times, along with vast computational resources, which are incompatible with the clinical workflow. Here, we present the Virtual Induction and Treatment of Arrhythmias (VITA), a novel, rapid and fully automated computational approach that uses reaction-Eikonal methodology to induce VT and identify subsequent ablation targets. The rationale for VITA is based on finding isosurfaces associated with an activation wavefront that splits in the ventricles due to the presence of an isolated isthmus of conduction within the scar; once identified, each isthmus may be assessed for their vulnerability to sustain a reentrant circuit, and the corresponding exit site automatically identified for potential ablation targeting. VITA was tested on a virtual cohort of 7 post-infarcted porcine hearts and the results compared to R-D simulations. Using only a standard desktop machine, VITA could detect all scar-related VTs, simulating activation time maps and ECGs (for clinical comparison) as well as computing ablation targets in 48 minutes. The comparable VTs probed by the R-D simulations took 68.5 hours on 256 cores of high-performance computing infrastructure. The set of lesions computed by VITA was shown to render the ventricular model VT-free. VITA could be used in near real-time as a complementary modality aiding in clinical decision-making in the treatment of post-infarction VTs.
Collapse
Affiliation(s)
- Fernando O Campos
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | | | - Caroline Mendonca Costa
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Mark O'Neill
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Guy's and St. Thomas' NHS Foundation Trust, Cardiovascular Directorate
| | - DanielScherr
- Division of Cardiology, Department of Internal Medicine, Medical University of Graz, Austria
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gernot Plank
- Gottfried Schatz Research Center Biophysics, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Graz, Austria
| | - Martin J Bishop
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
A Review on Atrial Fibrillation (Computer Simulation and Clinical Perspectives). HEARTS 2022. [DOI: 10.3390/hearts3010005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Atrial fibrillation (AF), a heart condition, has been a well-researched topic for the past few decades. This multidisciplinary field of study deals with signal processing, finite element analysis, mathematical modeling, optimization, and clinical procedure. This article is focused on a comprehensive review of journal articles published in the field of AF. Topics from the age-old fundamental concepts to specialized modern techniques involved in today’s AF research are discussed. It was found that a lot of research articles have already been published in modeling and simulation of AF. In comparison to that, the diagnosis and post-operative procedures for AF patients have not yet been totally understood or explored by the researchers. The simulation and modeling of AF have been investigated by many researchers in this field. Cellular model, tissue model, and geometric model among others have been used to simulate AF. Due to a very complex nature, the causes of AF have not been fully perceived to date, but the simulated results are validated with real-life patient data. Many algorithms have been proposed to detect the source of AF in human atria. There are many ablation strategies for AF patients, but the search for more efficient ablation strategies is still going on. AF management for patients with different stages of AF has been discussed in the literature as well but is somehow limited mostly to the patients with persistent AF. The authors hope that this study helps to find existing research gaps in the analysis and the diagnosis of AF.
Collapse
|
7
|
Mendonca Costa C, Gemmell P, Elliott MK, Whitaker J, Campos FO, Strocchi M, Neic A, Gillette K, Vigmond E, Plank G, Razavi R, O'Neill M, Rinaldi CA, Bishop MJ. Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction. Comput Biol Med 2022; 141:105061. [PMID: 34915331 PMCID: PMC8819160 DOI: 10.1016/j.compbiomed.2021.105061] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/04/2021] [Accepted: 11/20/2021] [Indexed: 12/01/2022]
Abstract
BACKGROUND Computational models of the heart built from cardiac MRI and electrophysiology (EP) data have shown promise for predicting the risk of and ablation targets for myocardial infarction (MI) related ventricular tachycardia (VT), as well as to predict paced activation sequences in heart failure patients. However, most recent studies have relied on low resolution imaging data and little or no EP personalisation, which may affect the accuracy of model-based predictions. OBJECTIVE To investigate the impact of model anatomy, MI scar morphology, and EP personalisation strategies on paced activation sequences and VT inducibility to determine the level of detail required to make accurate model-based predictions. METHODS Imaging and EP data were acquired from a cohort of six pigs with experimentally induced MI. Computational models of ventricular anatomy, incorporating MI scar, were constructed including bi-ventricular or left ventricular (LV) only anatomy, and MI scar morphology with varying detail. Tissue conductivities and action potential duration (APD) were fitted to 12-lead ECG data using the QRS duration and the QT interval, respectively, in addition to corresponding literature parameters. Paced activation sequences and VT induction were simulated. Simulated paced activation and VT inducibility were compared between models and against experimental data. RESULTS Simulations predict that the level of model anatomical detail has little effect on simulated paced activation, with all model predictions comparing closely with invasive EP measurements. However, detailed scar morphology from high-resolution images, bi-ventricular anatomy, and personalized tissue conductivities are required to predict experimental VT outcome. CONCLUSION This study provides clear guidance for model generation based on clinical data. While a representing high level of anatomical and scar detail will require high-resolution image acquisition, EP personalisation based on 12-lead ECG can be readily incorporated into modelling pipelines, as such data is widely available.
Collapse
Affiliation(s)
- Caroline Mendonca Costa
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK.
| | - Philip Gemmell
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Mark K Elliott
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - John Whitaker
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Fernando O Campos
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Marina Strocchi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | | | - Karli Gillette
- Gottfried Schatz Research Center, Biophysics, Medical University of Graz, Austria; Medical University of Graz, Austria and BioTechMed, Graz, Austria
| | - Edward Vigmond
- Institut de Rythmologie et de modélisation cardiaque (LIRYC), University of Bordeaux, France
| | - Gernot Plank
- Medical University of Graz, Austria and BioTechMed, Graz, Austria
| | - Reza Razavi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Mark O'Neill
- Department of Cardiology, Guy's and St Thomas' Hospital, London, UK
| | - Christopher A Rinaldi
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK; Department of Cardiology, Guy's and St Thomas' Hospital, London, UK
| | - Martin J Bishop
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| |
Collapse
|
8
|
Augustin CM, Gsell MA, Karabelas E, Willemen E, Prinzen FW, Lumens J, Vigmond EJ, Plank G. A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 386:114092. [PMID: 34630765 PMCID: PMC7611781 DOI: 10.1016/j.cma.2021.114092] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Computer models of cardiac electro-mechanics (EM) show promise as an effective means for the quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. To realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.
Collapse
Affiliation(s)
- Christoph M. Augustin
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A.F. Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Erik Willemen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Frits W. Prinzen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Edward J. Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
- Correspondence to: Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Neue Stiftingtalstrasse 6/IV, Graz 8010, Austria. (G. Plank)
| |
Collapse
|
9
|
Augustin CM, Gsell MAF, Karabelas E, Willemen E, Prinzen FW, Lumens J, Vigmond EJ, Plank G. A computationally efficient physiologically comprehensive 3D-0D closed-loop model of the heart and circulation. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 386:114092. [PMID: 34630765 DOI: 10.1016/jxma.2021.114092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Computer models of cardiac electro-mechanics (EM) show promise as an effective means for the quantitative analysis of clinical data and, potentially, for predicting therapeutic responses. To realize such advanced applications methodological key challenges must be addressed. Enhanced computational efficiency and robustness is crucial to facilitate, within tractable time frames, model personalization, the simulation of prolonged observation periods under a broad range of conditions, and physiological completeness encompassing therapy-relevant mechanisms is needed to endow models with predictive capabilities beyond the mere replication of observations. Here, we introduce a universal feature-complete cardiac EM modeling framework that builds on a flexible method for coupling a 3D model of bi-ventricular EM to the physiologically comprehensive 0D CircAdapt model representing atrial mechanics and closed-loop circulation. A detailed mathematical description is given and efficiency, robustness, and accuracy of numerical scheme and solver implementation are evaluated. After parameterization and stabilization of the coupled 3D-0D model to a limit cycle under baseline conditions, the model's ability to replicate physiological behaviors is demonstrated, by simulating the transient response to alterations in loading conditions and contractility, as induced by experimental protocols used for assessing systolic and diastolic ventricular properties. Mechanistic completeness and computational efficiency of this novel model render advanced applications geared towards predicting acute outcomes of EM therapies feasible.
Collapse
Affiliation(s)
- Christoph M Augustin
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A F Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Erik Willemen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Frits W Prinzen
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Joost Lumens
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux, France
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| |
Collapse
|
10
|
Electro-Mechanical Whole-Heart Digital Twins: A Fully Coupled Multi-Physics Approach. MATHEMATICS 2021. [DOI: 10.3390/math9111247] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Mathematical models of the human heart are evolving to become a cornerstone of precision medicine and support clinical decision making by providing a powerful tool to understand the mechanisms underlying pathophysiological conditions. In this study, we present a detailed mathematical description of a fully coupled multi-scale model of the human heart, including electrophysiology, mechanics, and a closed-loop model of circulation. State-of-the-art models based on human physiology are used to describe membrane kinetics, excitation-contraction coupling and active tension generation in the atria and the ventricles. Furthermore, we highlight ways to adapt this framework to patient specific measurements to build digital twins. The validity of the model is demonstrated through simulations on a personalized whole heart geometry based on magnetic resonance imaging data of a healthy volunteer. Additionally, the fully coupled model was employed to evaluate the effects of a typical atrial ablation scar on the cardiovascular system. With this work, we provide an adaptable multi-scale model that allows a comprehensive personalization from ion channels to the organ level enabling digital twin modeling.
Collapse
|
11
|
A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs. Med Image Anal 2021; 71:102080. [PMID: 33975097 DOI: 10.1016/j.media.2021.102080] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/15/2021] [Accepted: 04/06/2021] [Indexed: 12/21/2022]
Abstract
Cardiac digital twins (Cardiac Digital Twin (CDT)s) of human electrophysiology (Electrophysiology (EP)) are digital replicas of patient hearts derived from clinical data that match like-for-like all available clinical observations. Due to their inherent predictive potential, CDTs show high promise as a complementary modality aiding in clinical decision making and also in the cost-effective, safe and ethical testing of novel EP device therapies. However, current workflows for both the anatomical and functional twinning phases within CDT generation, referring to the inference of model anatomy and parameters from clinical data, are not sufficiently efficient, robust and accurate for advanced clinical and industrial applications. Our study addresses three primary limitations impeding the routine generation of high-fidelity CDTs by introducing; a comprehensive parameter vector encapsulating all factors relating to the ventricular EP; an abstract reference frame within the model allowing the unattended manipulation of model parameter fields; a novel fast-forward electrocardiogram (Electrocardiogram (ECG)) model for efficient and bio-physically-detailed simulation required for parameter inference. A novel workflow for the generation of CDTs is then introduced as an initial proof of concept. Anatomical twinning was performed within a reasonable time compatible with clinical workflows (<4h) for 12 subjects from clinically-attained magnetic resonance images. After assessment of the underlying fast forward ECG model against a gold standard bidomain ECG model, functional twinning of optimal parameters according to a clinically-attained 12 lead ECG was then performed using a forward Saltelli sampling approach for a single subject. The achieved results in terms of efficiency and fidelity demonstrate that our workflow is well-suited and viable for generating biophysically-detailed CDTs at scale.
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Grandits T, Gillette K, Neic A, Bayer J, Vigmond E, Pock T, Plank G. An Inverse Eikonal Method for Identifying Ventricular Activation Sequences from Epicardial Activation Maps. JOURNAL OF COMPUTATIONAL PHYSICS 2020; 419:109700. [PMID: 32952215 PMCID: PMC7116090 DOI: 10.1016/j.jcp.2020.109700] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
A key mechanism controlling cardiac function is the electrical activation sequence of the heart's main pumping chambers termed the ventricles. As such, personalization of the ventricular activation sequences is of pivotal importance for the clinical utility of computational models of cardiac electrophysiology. However, a direct observation of the activation sequence throughout the ventricular volume is virtually impossible. In this study, we report on a novel method for identification of activation sequences from activation maps measured at the outer surface of the heart termed the epicardium. Conceptually, the method attempts to identify the key factors governing the ventricular activation sequence - the timing of earliest activation sites (EAS) and the velocity tensor field within the ventricular walls - from sparse and noisy activation maps sampled from the epicardial surface and fits an Eikonal model to the observations. Regularization methods are first investigated to overcome the severe ill-posedness of the inverse problem in a simplified 2D example. These methods are then employed in an anatomically accurate biventricular model with two realistic activation models of varying complexity - a simplified trifascicular model (3F) and a topologically realistic model of the His-Purkinje system (HPS). Using epicardial activation maps at full resolution, we first demonstrate that reconstructing the volumetric activation sequence is, in principle, feasible under the assumption of known location of EAS and later evaluate robustness of the method against noise and reduced spatial resolution of observations. Our results suggest that the FIMIN algorithm is able to robustly recover the full 3D activation sequence using epicardial activation maps at a spatial resolution achievable with current mapping systems and in the presence of noise. Comparing the accuracy achieved in the reconstructed activation maps with clinical data uncertainties suggests that the FIMIN method may be suitable for the patient- specific parameterization of activation models.
Collapse
Affiliation(s)
- Thomas Grandits
- Institute of Computer Graphics and Vision, Graz University of Technology
- BioTechMed-Graz, Austria
| | - Karli Gillette
- Institute of Biophysics, Medical University of Graz
- BioTechMed-Graz, Austria
| | - Aurel Neic
- Institute of Biophysics, Medical University of Graz
| | - Jason Bayer
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux
| | - Thomas Pock
- Institute of Computer Graphics and Vision, Graz University of Technology
- BioTechMed-Graz, Austria
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz
- BioTechMed-Graz, Austria
| |
Collapse
|
14
|
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: 50] [Impact Index Per Article: 12.5] [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.
Collapse
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
| |
Collapse
|
15
|
Marx L, Gsell MAF, Rund A, Caforio F, Prassl AJ, Toth-Gayor G, Kuehne T, Augustin CM, Plank G. Personalization of electro-mechanical models of the pressure-overloaded left ventricle: fitting of Windkessel-type afterload models. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190342. [PMID: 32448067 PMCID: PMC7287328 DOI: 10.1098/rsta.2019.0342] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/01/2020] [Indexed: 05/21/2023]
Abstract
Computer models of left ventricular (LV) electro-mechanics (EM) show promise as a tool for assessing the impact of increased afterload upon LV performance. However, the identification of unique afterload model parameters and the personalization of EM LV models remains challenging due to significant clinical input uncertainties. Here, we personalized a virtual cohort of N = 17 EM LV models under pressure overload conditions. A global-local optimizer was developed to uniquely identify parameters of a three-element Windkessel (Wk3) afterload model. The sensitivity of Wk3 parameters to input uncertainty and of the EM LV model to Wk3 parameter uncertainty was analysed. The optimizer uniquely identified Wk3 parameters, and outputs of the personalized EM LV models showed close agreement with clinical data in all cases. Sensitivity analysis revealed a strong dependence of Wk3 parameters on input uncertainty. However, this had limited impact on outputs of EM LV models. A unique identification of Wk3 parameters from clinical data appears feasible, but it is sensitive to input uncertainty, thus depending on accurate invasive measurements. By contrast, the EM LV model outputs were less sensitive, with errors of less than 8.14% for input data errors of 10%, which is within the bounds of clinical data uncertainty. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
Collapse
Affiliation(s)
- Laura Marx
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Matthias A. F. Gsell
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Armin Rund
- Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria
| | - Federica Caforio
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Anton J. Prassl
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Gabor Toth-Gayor
- Department of Cardiology, Medical University Graz, Graz, Austria
| | - Titus Kuehne
- Institute for Cardiovascular Computer-assisted Medicine (ICM), Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Imaging and Congenital Heart Disease, German Heart Center Berlin, Berlin, Germany
| | - Christoph M. Augustin
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging - Division of Biophysics, Medical University Graz, Graz, Austria
- e-mail:
| |
Collapse
|
16
|
Huellebrand M, Messroghli D, Tautz L, Kuehne T, Hennemuth A. An extensible software platform for interdisciplinary cardiovascular imaging research. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105277. [PMID: 31891904 DOI: 10.1016/j.cmpb.2019.105277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 11/21/2019] [Accepted: 12/11/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiovascular imaging is an exponentially growing field with aspects ranging from image acquisition and analysis to disease characterization, and evaluation of therapy approaches.The transfer of innovative new technological and algorithmic solutions into clinical practice is still slow. In addition to the verification of solutions, their integration in the clinical processing workflow must be enabled for the assessment of clinical impact and risks. The goal of our software platform for cardiac image processing - CAIPI - is to support researchers from different specialties such as imaging physics, computer science, and medicine by a common extensible platform to address typical challenges and hurdles in interdisciplinary cardiovascular imaging research. It provides an integrated solution for method comparison, integrated analysis, and validation in the clinical context. The interface concept enables a combination with existing frameworks that address specific aspects of the pipeline, such as modeling (e.g., OpenCMISS, CARP) or image reconstruction (Gadgetron). METHODS In our platform, we developed a concept for import, integration, and management of cardiac image data. The integration approach considers the spatiotemporal properties of the beating heart through a specific data model. The solution is based on MeVisLab and provides functionalities for data retrieval and storage. Two types of plugins can be added. While ToolPlugins usually provide processing algorithms such as image correction and segmentation, AnalysisPlugins enable interactive data exploration and reporting. GUI integration concepts are presented for both plugin types. We developed domain-specific reporting and visualization tools (e.g., AHA segment model) to enable validation studies by clinical experts. The platform offers plugins for calculating and reporting quantitative parameters such as cardiac function, which can be used to, e.g., evaluate the effect of processing algorithms on clinical parameters. Export functionalities include quantitative measurements to Excel, image data to PACS, and STL models to modeling and simulation tools. RESULTS To demonstrate the applicability of this concept both for method development and clinical application, we present use cases representing different problems along the innovation chain in cardiac MR imaging. Validation of an image reconstruction method (MRI T1 mapping) Validation of an image correction method for real-time 2D-PC MRI Comparison of quantification methods for blood flow analysis Training and integration of machine learning solutions with expert annotations Clinical studies with new imaging techniques (flow measurements in the carotid arteries and peripheral veins as well as cerebral spinal fluid). CONCLUSION The presented platform can be used in interdisciplinary teams, in which engineers or data scientists perform the method validation, followed by clinical research studies in patient collectives. The demonstrated use cases show how it enables the transfer of innovations through validation in the cardiovascular application context.
Collapse
Affiliation(s)
- Markus Huellebrand
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany; Fraunhofer MEVIS, Bremen, Germany.
| | - Daniel Messroghli
- Department of Internal Medicine and Cardiology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Internal Medicine - Cardiology, Deutsches Herzzentrum Berlin, Berlin, Germany; German Center for Cardiovascular Research (DZHK), partner site Berlin
| | - Lennart Tautz
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany; Fraunhofer MEVIS, Bremen, Germany
| | - Titus Kuehne
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany; German Center for Cardiovascular Research (DZHK), partner site Berlin; Department of Congenital Heart Disease and Paediatric Cardiology, Deutsches Herzzentrum Berlin, Berlin, Germany
| | - Anja Hennemuth
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany; Fraunhofer MEVIS, Bremen, Germany; German Center for Cardiovascular Research (DZHK), partner site Berlin
| |
Collapse
|
17
|
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.
Collapse
|
18
|
Neic A, Gsell MA, Karabelas E, Prassl AJ, Plank G. Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool. SOFTWAREX 2020; 11:100454. [PMID: 32607406 PMCID: PMC7326605 DOI: 10.1016/j.softx.2020.100454] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Advanced cardiac modeling studies rely on the ability to generate and functionalize personalized in silico models from tomographic multi-label image stacks. Eventually, this is used for building virtual cohorts that capture the variability in size, shape, and morphology of individual hearts. Typical modeling workflows involve a multitude of interactive mesh manipulation steps, rendering model generation expensive. Meshtool is software specifically designed for automating all complex mesh manipulation tasks emerging in such workflows by implementing algorithms for tasks describable as operations on label fields and/or geometric features. We illustrate how Meshtool increases efficiency and reduces costs by offering an automatable, high performance mesh manipulation toolbox.
Collapse
Affiliation(s)
- Aurel Neic
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- NumeriCor GmbH, Graz, Austria
| | - Matthias A.F. Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Elias Karabelas
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Anton J. Prassl
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
| |
Collapse
|
19
|
Factors Promoting Conduction Slowing as Substrates for Block and Reentry in Infarcted Hearts. Biophys J 2019; 117:2361-2374. [PMID: 31521328 PMCID: PMC6990374 DOI: 10.1016/j.bpj.2019.08.008] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 07/03/2019] [Accepted: 08/05/2019] [Indexed: 01/11/2023] Open
Abstract
The development of effective and safe therapies for scar-related ventricular tachycardias requires a detailed understanding of the mechanisms underlying the conduction block that initiates electrical re-entries associated with these arrhythmias. Conduction block has been often associated with electrophysiological changes that prolong action potential duration (APD) within the border zone (BZ) of chronically infarcted hearts. However, experimental evidence suggests that remodeling processes promoting conduction slowing as opposed to APD prolongation mark the chronic phase. In this context, the substrate for the initial block at the mouth of an isthmus/diastolic channel leading to ventricular tachycardia is unclear. The goal of this study was to determine whether electrophysiological parameters associated with conduction slowing can cause block and re-entry in the BZ. In silico experiments were conducted on two-dimensional idealized infarct tissue as well as on a cohort of postinfarction porcine left ventricular models constructed from ex vivo magnetic resonance imaging scans. Functional conduction slowing in the BZ was modeled by reducing sodium current density, whereas structural conduction slowing was represented by decreasing tissue conductivity and including fibrosis. The arrhythmogenic potential of APD prolongation was also tested as a basis for comparison. Within all models, the combination of reduced sodium current with structural remodeling more often degenerated into re-entry and, if so, was more likely to be sustained for more cycles. Although re-entries were also detected in experiments with prolonged APD, they were often not sustained because of the subsequent block caused by long-lasting repolarization. Functional and structural conditions associated with slow conduction rather than APD prolongation form a potent substrate for arrhythmogenesis at the isthmus/BZ of chronically infarcted hearts. Reduced excitability led to block while slow conduction shortened the wavelength of propagation, facilitating the sustenance of re-entries. These findings provide important insights for models of patient-specific risk stratification and therapy planning.
Collapse
|
20
|
Effects of left ventricle wall thickness uncertainties on cardiac mechanics. Biomech Model Mechanobiol 2019; 18:1415-1427. [PMID: 31025130 DOI: 10.1007/s10237-019-01153-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 04/19/2019] [Indexed: 01/22/2023]
Abstract
Computational models of the heart have reached a level of maturity that enables sophisticated patient-specific simulations and hold potential for important applications in diagnosis and therapy planning. However, such clinical use puts strict demands on the reliability and accuracy of the models and requires the sensitivity of the model predictions due to errors and uncertainty in the model inputs to be quantified. The models typically contain a large number of parameters, which are difficult to measure and therefore associated with considerable uncertainty. Additionally, patient-specific geometries are usually constructed by semi-manual processing of medical images and must be assumed to be a potential source of model uncertainty. In this paper, we assess the model accuracy by considering the impact of geometrical uncertainties, which typically occur in image-based computational geometries. An approach based on 17 AHA segments diagram is used to consider uncertainties in wall thickness and also in the material properties and fiber orientation, and we perform a comprehensive uncertainty quantification and sensitivity analysis based on polynomial chaos expansions. The quantities considered include stress, strain and global deformation parameters of the left ventricle. The results indicate that important quantities of interest may be more affected by wall thickness, and highlight the need for accurate geometry reconstructions in patient-specific cardiac mechanics models.
Collapse
|
21
|
Doste R, Soto-Iglesias D, Bernardino G, Alcaine A, Sebastian R, Giffard-Roisin S, Sermesant M, Berruezo A, Sanchez-Quintana D, Camara O. A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3185. [PMID: 30721579 DOI: 10.1002/cnm.3185] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 10/23/2018] [Accepted: 01/05/2019] [Indexed: 06/09/2023]
Abstract
Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum, and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modeling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modeling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.
Collapse
Affiliation(s)
- Ruben Doste
- Physense, ETIC, Universitat Pompeu Fabra, Barcelona, Spain
| | | | | | | | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | | | | | - Antonio Berruezo
- Arrhythmia Section, Cardiology Department, Thorax Institute, Hospital Clinic, Universitat de Barcelona, Barcelona, Spain
| | - Damian Sanchez-Quintana
- Department of Anatomy and Cell Biology, Faculty of Medicine, University of Extremadura, Badajoz, Spain
| | - Oscar Camara
- Physense, ETIC, Universitat Pompeu Fabra, Barcelona, Spain
| |
Collapse
|
22
|
Barbarotta L, Rossi S, Dedè L, Quarteroni A. A transmurally heterogeneous orthotropic activation model for ventricular contraction and its numerical validation. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e3137. [PMID: 30070071 DOI: 10.1002/cnm.3137] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 06/19/2018] [Accepted: 07/22/2018] [Indexed: 06/08/2023]
Abstract
Models for cardiac mechanics require an activation mechanism properly representing the stress-strain relations in the contracting myocardium. In this paper, we propose a new activation model that accounts for the transmural heterogeneities observed in myocardial strain measurements. In order to take the anisotropy of the active mechanics into account, our model is based on an active strain formulation. Thanks to multiplicative decomposition of the deformation gradient tensor, in this formulation, the active strains orthogonal to the fibers can be naturally described. We compare the results of our novel formulation against different anisotropic models of the active contraction of the cardiac muscle, as well as against experimental data available in the literature. We show that with the currently available models, the strain distributions are not in agreement with the reported experimental measurements. Conversely, we show that our new transmurally heterogeneous orthotropic activation model improves the accuracy of shear strains related to in-plane rotations and torsion.
Collapse
Affiliation(s)
- Luca Barbarotta
- Department of Biomedical Engineering, Technische Universiteit Eindhoven, Eindhoven, The Netherlands
| | - Simone Rossi
- Department of Mathematics, University of North Carolina, Chapel Hill, North Carolina
| | - Luca Dedè
- Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Alfio Quarteroni
- Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
- Institute of Mathematics, Ećole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| |
Collapse
|
23
|
Gsell MAF, Augustin CM, Prassl AJ, Karabelas E, Fernandes JF, Kelm M, Goubergrits L, Kuehne T, Plank G. Assessment of wall stresses and mechanical heart power in the left ventricle: Finite element modeling versus Laplace analysis. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e3147. [PMID: 30151998 PMCID: PMC6492182 DOI: 10.1002/cnm.3147] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 07/19/2018] [Accepted: 08/14/2018] [Indexed: 05/30/2023]
Abstract
INTRODUCTION Stenotic aortic valve disease (AS) causes pressure overload of the left ventricle (LV) that may trigger adverse remodeling and precipitate progression towards heart failure (HF). As myocardial energetics can be impaired during AS, LV wall stresses and biomechanical power provide a complementary view of LV performance that may aide in better assessing the state of disease. OBJECTIVES Using a high-resolution electro-mechanical (EM) in silico model of the LV as a reference, we evaluated clinically feasible Laplace-based methods for assessing global LV wall stresses and biomechanical power. METHODS We used N = 4 in silico finite element (FE) EM models of LV and aorta of patients suffering from AS. All models were personalized with clinical data under pretreatment conditions. Left ventricle wall stresses and biomechanical power were computed accurately from FE kinematic data and compared with Laplace-based estimation methods, which were applied to the same FE model data. RESULTS AND CONCLUSION Laplace estimates of LV wall stress are able to provide a rough approximation of global mean stress in the circumferential-longitudinal plane of the LV. However, according to FE results, spatial heterogeneity of stresses in the LV wall is significant, leading to major discrepancies between local stresses and global mean stress. Assessment of mechanical power with Laplace methods is feasible, but these are inferior in accuracy compared with FE models. The accurate assessment of stress and power density distribution in the LV wall is only feasible based on patient-specific FE modeling.
Collapse
Affiliation(s)
| | - Christoph M. Augustin
- Institute of BiophysicsMedical University of GrazGrazAustria
- Department of Mechanical EngineeringUniversity of CaliforniaBerkleyCalifornia
| | - Anton J. Prassl
- Institute of BiophysicsMedical University of GrazGrazAustria
| | - Elias Karabelas
- Institute of BiophysicsMedical University of GrazGrazAustria
| | - Joao F. Fernandes
- Institute for Cardiovascular Computer‐assisted MedicineCharité ‐ Universitätsmedizin BerlinBerlinGermany
| | - Marcus Kelm
- Institute for Cardiovascular Computer‐assisted MedicineCharité ‐ Universitätsmedizin BerlinBerlinGermany
- Department of Congenital Heart Disease/Pediatric CardiologyGerman Heart Institute BerlinBerlinGermany
| | - Leonid Goubergrits
- Institute for Cardiovascular Computer‐assisted MedicineCharité ‐ Universitätsmedizin BerlinBerlinGermany
| | - Titus Kuehne
- Institute for Cardiovascular Computer‐assisted MedicineCharité ‐ Universitätsmedizin BerlinBerlinGermany
- Department of Congenital Heart Disease/Pediatric CardiologyGerman Heart Institute BerlinBerlinGermany
| | - Gernot Plank
- Institute of BiophysicsMedical University of GrazGrazAustria
| |
Collapse
|
24
|
Zhong L, Zhang JM, Su B, Tan RS, Allen JC, Kassab GS. Application of Patient-Specific Computational Fluid Dynamics in Coronary and Intra-Cardiac Flow Simulations: Challenges and Opportunities. Front Physiol 2018; 9:742. [PMID: 29997520 PMCID: PMC6028770 DOI: 10.3389/fphys.2018.00742] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 05/28/2018] [Indexed: 12/13/2022] Open
Abstract
The emergence of new cardiac diagnostics and therapeutics of the heart has given rise to the challenging field of virtual design and testing of technologies in a patient-specific environment. Given the recent advances in medical imaging, computational power and mathematical algorithms, patient-specific cardiac models can be produced from cardiac images faster, and more efficiently than ever before. The emergence of patient-specific computational fluid dynamics (CFD) has paved the way for the new field of computer-aided diagnostics. This article provides a review of CFD methods, challenges and opportunities in coronary and intra-cardiac flow simulations. It includes a review of market products and clinical trials. Key components of patient-specific CFD are covered briefly which include image segmentation, geometry reconstruction, mesh generation, fluid-structure interaction, and solver techniques.
Collapse
Affiliation(s)
- Liang Zhong
- National Heart Centre Singapore, National Heart Research Institute of Singapore, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Jun-Mei Zhang
- National Heart Centre Singapore, National Heart Research Institute of Singapore, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Boyang Su
- National Heart Centre Singapore, National Heart Research Institute of Singapore, Singapore, Singapore
| | - Ru San Tan
- National Heart Centre Singapore, National Heart Research Institute of Singapore, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | | | - Ghassan S Kassab
- California Medical Innovations Institute, San Diego, CA, United States
| |
Collapse
|
25
|
Karabelas E, Gsell MAF, Augustin CM, Marx L, Neic A, Prassl AJ, Goubergrits L, Kuehne T, Plank G. Towards a Computational Framework for Modeling the Impact of Aortic Coarctations Upon Left Ventricular Load. Front Physiol 2018; 9:538. [PMID: 29892227 PMCID: PMC5985756 DOI: 10.3389/fphys.2018.00538] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 04/26/2018] [Indexed: 01/04/2023] Open
Abstract
Computational fluid dynamics (CFD) models of blood flow in the left ventricle (LV) and aorta are important tools for analyzing the mechanistic links between myocardial deformation and flow patterns. Typically, the use of image-based kinematic CFD models prevails in applications such as predicting the acute response to interventions which alter LV afterload conditions. However, such models are limited in their ability to analyze any impacts upon LV load or key biomarkers known to be implicated in driving remodeling processes as LV function is not accounted for in a mechanistic sense. This study addresses these limitations by reporting on progress made toward a novel electro-mechano-fluidic (EMF) model that represents the entire physics of LV electromechanics (EM) based on first principles. A biophysically detailed finite element (FE) model of LV EM was coupled with a FE-based CFD solver for moving domains using an arbitrary Eulerian-Lagrangian (ALE) formulation. Two clinical cases of patients suffering from aortic coarctations (CoA) were built and parameterized based on clinical data under pre-treatment conditions. For one patient case simulations under post-treatment conditions after geometric repair of CoA by a virtual stenting procedure were compared against pre-treatment results. Numerical stability of the approach was demonstrated by analyzing mesh quality and solver performance under the significantly large deformations of the LV blood pool. Further, computational tractability and compatibility with clinical time scales were investigated by performing strong scaling benchmarks up to 1536 compute cores. The overall cost of the entire workflow for building, fitting and executing EMF simulations was comparable to those reported for image-based kinematic models, suggesting that EMF models show potential of evolving into a viable clinical research tool.
Collapse
Affiliation(s)
- Elias Karabelas
- Computational Cardiology Laboratory, Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Matthias A F Gsell
- Computational Cardiology Laboratory, Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Christoph M Augustin
- Computational Cardiology Laboratory, Institute of Biophysics, Medical University of Graz, Graz, Austria.,Shadden Research Group, Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States
| | - Laura Marx
- Computational Cardiology Laboratory, Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Aurel Neic
- Computational Cardiology Laboratory, Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Anton J Prassl
- Computational Cardiology Laboratory, Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Leonid Goubergrits
- Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Berlin, Germany.,Institute for Imaging Science and Computational Modeling in Cardiovascular Medicine, Charité - University Medicine Berlin, Berlin, Germany
| | - Titus Kuehne
- Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Berlin, Germany.,Institute for Imaging Science and Computational Modeling in Cardiovascular Medicine, Charité - University Medicine Berlin, Berlin, Germany
| | - Gernot Plank
- Computational Cardiology Laboratory, Institute of Biophysics, Medical University of Graz, Graz, Austria
| |
Collapse
|
26
|
Fastl TE, Tobon-Gomez C, Crozier A, Whitaker J, Rajani R, McCarthy KP, Sanchez-Quintana D, Ho SY, O'Neill MD, Plank G, Bishop MJ, Niederer SA. Personalized computational modeling of left atrial geometry and transmural myofiber architecture. Med Image Anal 2018; 47:180-190. [PMID: 29753182 PMCID: PMC6277816 DOI: 10.1016/j.media.2018.04.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 03/27/2018] [Accepted: 04/03/2018] [Indexed: 01/15/2023]
Abstract
Atrial fibrillation (AF) is a supraventricular tachyarrhythmia characterized by complete absence of coordinated atrial contraction and is associated with an increased morbidity and mortality. Personalized computational modeling provides a novel framework for integrating and interpreting the role of atrial electrophysiology (EP) including the underlying anatomy and microstructure in the development and sustenance of AF. Coronary computed tomography angiography data were segmented using a statistics-based approach and the smoothed voxel representations were discretized into high-resolution tetrahedral finite element (FE) meshes. To estimate the complex left atrial myofiber architecture, individual fiber fields were generated according to morphological data on the endo- and epicardial surfaces based on local solutions of Laplace’s equation and transmurally interpolated to tetrahedral elements. The influence of variable transmural microstructures was quantified through EP simulations on 3 patients using 5 different fiber interpolation functions. Personalized geometrical models included the heterogeneous thickness distribution of the left atrial myocardium and subsequent discretization led to high-fidelity tetrahedral FE meshes. The novel algorithm for automated incorporation of the left atrial fiber architecture provided a realistic estimate of the atrial microstructure and was able to qualitatively capture all important fiber bundles. Consistent maximum local activation times were predicted in EP simulations using individual transmural fiber interpolation functions for each patient suggesting a negligible effect of the transmural myofiber architecture on EP. The established modeling pipeline provides a robust framework for the rapid development of personalized model cohorts accounting for detailed anatomy and microstructure and facilitates simulations of atrial EP.
Collapse
Affiliation(s)
- Thomas E Fastl
- Department of Biomedical Engineering, King's College London, London, United Kingdom.
| | - Catalina Tobon-Gomez
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Andrew Crozier
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - John Whitaker
- Department of Biomedical Engineering, King's College London, London, United Kingdom; Department of Cardiology, Guy's and St Thomas' Hospitals, London, United Kingdom
| | - Ronak Rajani
- Department of Biomedical Engineering, King's College London, London, United Kingdom; Department of Cardiology, Guy's and St Thomas' Hospitals, London, United Kingdom
| | - Karen P McCarthy
- Cardiac Morphology Unit, Royal Brompton Hospital, London, United Kingdom
| | | | - Siew Y Ho
- Cardiac Morphology Unit, Royal Brompton Hospital, London, United Kingdom
| | - Mark D O'Neill
- Department of Biomedical Engineering, King's College London, London, United Kingdom; Department of Cardiology, Guy's and St Thomas' Hospitals, London, United Kingdom
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Martin J Bishop
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Steven A Niederer
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| |
Collapse
|
27
|
Whitaker J, Fish J, Harrison J, Chubb H, Williams SE, Fastl T, Corrado C, Van Zaen J, Gibbs J, O’Neill L, Mukherjee R, Rittey D, Thorsten J, Donskoy E, Sohal M, Rajani R, Niederer S, Wright M, O’Neill MD. Lesion Index–Guided Ablation Facilitates Continuous, Transmural, and Durable Lesions in a Porcine Recovery Model. Circ Arrhythm Electrophysiol 2018; 11:e005892. [DOI: 10.1161/circep.117.005892] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 02/22/2018] [Indexed: 11/16/2022]
Affiliation(s)
- John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Jeffrey Fish
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - James Harrison
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Henry Chubb
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Steven E. Williams
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Thomas Fastl
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Cesare Corrado
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Jérôme Van Zaen
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Jennifer Gibbs
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Louisa O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Rahul Mukherjee
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Dianna Rittey
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Jason Thorsten
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Elina Donskoy
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Manav Sohal
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Ronak Rajani
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Steve Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Matthew Wright
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| | - Mark D. O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, United Kingdom (J.W., J.H., H.C., S.E.W., T.F., C.C., L.O., R.M., M.S., R.R., S.N., M.W., M.O.N.). Abbott, St. Paul, MN (J.F., J.V.Z., J.G., D.R.). Scientific Solutions LLC, Minneapolis, MN (J.T.). Pathology Consultant Services, Hartford, CT (E.D.)
| |
Collapse
|
28
|
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.
Collapse
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
| |
Collapse
|
29
|
DAO TIENTUAN, FAN ANGXIAO, DAKPÉ STÉPHANIE, POULETAUT PHILIPPE, RACHIK MOHAMED, HO BA THO MARIECHRISTINE. IMAGE-BASED SKELETAL MUSCLE COORDINATION: CASE STUDY ON A SUBJECT SPECIFIC FACIAL MIMIC SIMULATION. J MECH MED BIOL 2018. [DOI: 10.1142/s0219519418500203] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Facial muscle coordination is a fundamental mechanism for facial mimics and expressions. The understanding of this complex mechanism leads to better diagnosis and treatment of facial disorders like facial palsy or disfigurement. The objective of this work was to use magnetic resonance imaging (MRI) technique to characterize the activation behavior of facial muscles and then simulate their coordination mechanism using a subject specific finite element model. MRI data of lower head of a healthy subject were acquired in neutral and in the pronunciation of the sound [o] positions. Then, a finite element model was derived directly from acquired MRI images in neutral position. Transversely-isotropic, hyperelastic, quasi-incompressible behavior law was implemented for modeling facial muscles. The simulation to produce the pronunciation of the sound [o] was performed by the cumulative coordination between three pairs of facial mimic muscles (Zygomaticus Major (ZM), Levator Labii Superioris (LLS), Levator Anguli Oris (LAO)). Mean displacement amplitude showed a good agreement with a relative deviation of 15% between numerical outcome and MRI-based measurement when all three muscles are involved. This study elucidates, for the first time, the facial muscle coordination using in vivo data leading to improve the model understanding and simulation outcomes.
Collapse
Affiliation(s)
- TIEN TUAN DAO
- Sorbonne University, Université de technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de recherche Royallieu, CS 60 319 Compiègne, France
| | - ANG-XIAO FAN
- Sorbonne University, Université de technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de recherche Royallieu, CS 60 319 Compiègne, France
| | - STÉPHANIE DAKPÉ
- Sorbonne University, Université de technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de recherche Royallieu, CS 60 319 Compiègne, France
| | - PHILIPPE POULETAUT
- Sorbonne University, Université de technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de recherche Royallieu, CS 60 319 Compiègne, France
| | - MOHAMED RACHIK
- Sorbonne University, Université de technologie de Compiègne, CNRS, UMR 7337 Roberval, Centre de recherche Royallieu - CS 60 319 - 60 203, Compiègne cedex, France
| | - MARIE CHRISTINE HO BA THO
- Sorbonne University, Université de technologie de Compiègne, CNRS, UMR 7338 Biomechanics and Bioengineering, Centre de recherche Royallieu, CS 60 319 Compiègne, France
| |
Collapse
|
30
|
Neic A, Campos FO, Prassl AJ, Niederer SA, Bishop MJ, Vigmond EJ, Plank G. Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model. JOURNAL OF COMPUTATIONAL PHYSICS 2017; 346:191-211. [PMID: 28819329 PMCID: PMC5555399 DOI: 10.1016/j.jcp.2017.06.020] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Anatomically accurate and biophysically detailed bidomain models of the human heart have proven a powerful tool for gaining quantitative insight into the links between electrical sources in the myocardium and the concomitant current flow in the surrounding medium as they represent their relationship mechanistically based on first principles. Such models are increasingly considered as a clinical research tool with the perspective of being used, ultimately, as a complementary diagnostic modality. An important prerequisite in many clinical modeling applications is the ability of models to faithfully replicate potential maps and electrograms recorded from a given patient. However, while the personalization of electrophysiology models based on the gold standard bidomain formulation is in principle feasible, the associated computational expenses are significant, rendering their use incompatible with clinical time frames. In this study we report on the development of a novel computationally efficient reaction-eikonal (R-E) model for modeling extracellular potential maps and electrograms. Using a biventricular human electrophysiology model, which incorporates a topologically realistic His-Purkinje system (HPS), we demonstrate by comparing against a high-resolution reaction-diffusion (R-D) bidomain model that the R-E model predicts extracellular potential fields, electrograms as well as ECGs at the body surface with high fidelity and offers vast computational savings greater than three orders of magnitude. Due to their efficiency R-E models are ideally suitable for forward simulations in clinical modeling studies which attempt to personalize electrophysiological model features.
Collapse
Affiliation(s)
- Aurel Neic
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Fernando O. Campos
- Institute of Biophysics, Medical University of Graz, Graz, Austria
- Dept. of Congenital Heart Diseases and Pediatric Cardiology, German Heart Institute Berlin, Berlin, Germany
| | - Anton J. Prassl
- Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Steven A. Niederer
- Dept. Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College of London, London, United Kingdom
| | - Martin J. Bishop
- Dept. Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College of London, London, United Kingdom
| | | | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Graz, Austria
- Corresponding author. (G. Plank)
| |
Collapse
|
31
|
Cinelli I, Destrade M, Duffy M, McHugh P. Electrothermal Equivalent Three-Dimensional Finite-Element Model of a Single Neuron. IEEE Trans Biomed Eng 2017; 65:1373-1381. [PMID: 28920894 DOI: 10.1109/tbme.2017.2752258] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE We propose a novel approach for modelling the interdependence of electrical and mechanical phenomena in nervous cells, by using electrothermal equivalences in finite element (FE) analysis so that existing thermomechanical tools can be applied. METHODS First, the equivalence between electrical and thermal properties of the nerve materials is established, and results of a pure heat conduction analysis performed in Abaqus CAE Software 6.13-3 are validated with analytical solutions for a range of steady and transient conditions. This validation includes the definition of equivalent active membrane properties that enable prediction of the action potential. Then, as a step toward fully coupled models, electromechanical coupling is implemented through the definition of equivalent piezoelectric properties of the nerve membrane using the thermal expansion coefficient, enabling prediction of the mechanical response of the nerve to the action potential. RESULTS Results of the coupled electromechanical model are validated with previously published experimental results of deformation for squid giant axon, crab nerve fibre, and garfish olfactory nerve fibre. CONCLUSION A simplified coupled electromechanical modelling approach is established through an electrothermal equivalent FE model of a nervous cell for biomedical applications. SIGNIFICANCE One of the key findings is the mechanical characterization of the neural activity in a coupled electromechanical domain, which provides insights into the electromechanical behaviour of nervous cells, such as thinning of the membrane. This is a first step toward modelling three-dimensional electromechanical alteration induced by trauma at nerve bundle, tissue, and organ levels.
Collapse
|
32
|
Augustin CM, Crozier A, Neic A, Prassl AJ, Karabelas E, Ferreira da Silva T, Fernandes JF, Campos F, Kuehne T, Plank G. Patient-specific modeling of left ventricular electromechanics as a driver for haemodynamic analysis. Europace 2017; 18:iv121-iv129. [PMID: 28011839 PMCID: PMC5386137 DOI: 10.1093/europace/euw369] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 08/26/2016] [Indexed: 01/30/2023] Open
Abstract
Aims Models of blood flow in the left ventricle (LV) and aorta are an important tool for analysing the interplay between LV deformation and flow patterns. Typically, image-based kinematic models describing endocardial motion are used as an input to blood flow simulations. While such models are suitable for analysing the hemodynamic status quo, they are limited in predicting the response to interventions that alter afterload conditions. Mechano-fluidic models using biophysically detailed electromechanical (EM) models have the potential to overcome this limitation, but are more costly to build and compute. We report our recent advancements in developing an automated workflow for the creation of such CFD ready kinematic models to serve as drivers of blood flow simulations. Methods and results EM models of the LV and aortic root were created for four pediatric patients treated for either aortic coarctation or aortic valve disease. Using MRI, ECG and invasive pressure recordings, anatomy as well as electrophysiological, mechanical and circulatory model components were personalized. Results The implemented modeling pipeline was highly automated and allowed model construction and execution of simulations of a patient’s heartbeat within 1 day. All models reproduced clinical data with acceptable accuracy. Conclusion Using the developed modeling workflow, the use of EM LV models as driver of fluid flow simulations is becoming feasible. While EM models are costly to construct, they constitute an important and nontrivial step towards fully coupled electro-mechano-fluidic (EMF) models and show promise as a tool for predicting the response to interventions which affect afterload conditions.
Collapse
Affiliation(s)
- Christoph M Augustin
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria.,Department of Mechanical Engineering, University of California, 5126 Etcheverry Hall, Berkeley, CA 94720, USA
| | - Andrew Crozier
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
| | - Aurel Neic
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
| | - Anton J Prassl
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
| | - Elias Karabelas
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
| | - Tiago Ferreira da Silva
- Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Joao F Fernandes
- Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Fernando Campos
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria.,Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Titus Kuehne
- Department of Congenital Heart Disease/Pediatric Cardiology, German Heart Institute Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Gernot Plank
- Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria
| |
Collapse
|
33
|
Boyle PM, Zahid S, Trayanova NA. Using personalized computer models to custom-tailor ablation procedures for atrial fibrillation patients: are we there yet? Expert Rev Cardiovasc Ther 2017; 15:339-341. [PMID: 28395557 DOI: 10.1080/14779072.2017.1317593] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Patrick M Boyle
- a Institute for Computational Medicine and Department of Biomedical Engineering , Johns Hopkins University , Baltimore , USA
| | - Sohail Zahid
- a Institute for Computational Medicine and Department of Biomedical Engineering , Johns Hopkins University , Baltimore , USA
| | - Natalia A Trayanova
- a Institute for Computational Medicine and Department of Biomedical Engineering , Johns Hopkins University , Baltimore , USA
| |
Collapse
|
34
|
Fu F, Qin Z, Xu C, Chen XY, Li RX, Wang LN, Peng DW, Sun HT, Tu Y, Chen C, Zhang S, Zhao ML, Li XH. Magnetic resonance imaging-three-dimensional printing technology fabricates customized scaffolds for brain tissue engineering. Neural Regen Res 2017; 12:614-622. [PMID: 28553343 PMCID: PMC5436361 DOI: 10.4103/1673-5374.205101] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Conventional fabrication methods lack the ability to control both macro- and micro-structures of generated scaffolds. Three-dimensional printing is a solid free-form fabrication method that provides novel ways to create customized scaffolds with high precision and accuracy. In this study, an electrically controlled cortical impactor was used to induce randomized brain tissue defects. The overall shape of scaffolds was designed using rat-specific anatomical data obtained from magnetic resonance imaging, and the internal structure was created by computer-aided design. As the result of limitations arising from insufficient resolution of the manufacturing process, we magnified the size of the cavity model prototype five-fold to successfully fabricate customized collagen-chitosan scaffolds using three-dimensional printing. Results demonstrated that scaffolds have three-dimensional porous structures, high porosity, highly specific surface areas, pore connectivity and good internal characteristics. Neural stem cells co-cultured with scaffolds showed good viability, indicating good biocompatibility and biodegradability. This technique may be a promising new strategy for regenerating complex damaged brain tissues, and helps pave the way toward personalized medicine.
Collapse
Affiliation(s)
- Feng Fu
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Zhe Qin
- Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China
| | - Chao Xu
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Xu-Yi Chen
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Rui-Xin Li
- Institute of Medical Equipment, The Academy of Military Medical Sciences, Tianjin, China
| | - Li-Na Wang
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Ding-Wei Peng
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Hong-Tao Sun
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Yue Tu
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Chong Chen
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Sai Zhang
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Ming-Liang Zhao
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| | - Xiao-Hong Li
- Institute of Traumatic Brain Injury and Neurology, Pingjin Hospital, Logistics University of Chinese People's Armed Police Forces, Tianjin, China.,Key Laboratory of Neurotrauma Repair of Tianjin, Tianjin, China
| |
Collapse
|
35
|
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.
Collapse
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
| |
Collapse
|
36
|
Hill AP, Perry MD, Abi-Gerges N, Couderc JP, Fermini B, Hancox JC, Knollmann BC, Mirams GR, Skinner J, Zareba W, Vandenberg JI. Computational cardiology and risk stratification for sudden cardiac death: one of the grand challenges for cardiology in the 21st century. J Physiol 2016; 594:6893-6908. [PMID: 27060987 PMCID: PMC5134408 DOI: 10.1113/jp272015] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 03/16/2016] [Indexed: 12/25/2022] Open
Abstract
Risk stratification in the context of sudden cardiac death has been acknowledged as one of the major challenges facing cardiology for the past four decades. In recent years, the advent of high performance computing has facilitated organ-level simulation of the heart, meaning we can now examine the causes, mechanisms and impact of cardiac dysfunction in silico. As a result, computational cardiology, largely driven by the Physiome project, now stands at the threshold of clinical utility in regards to risk stratification and treatment of patients at risk of sudden cardiac death. In this white paper, we outline a roadmap of what needs to be done to make this translational step, using the relatively well-developed case of acquired or drug-induced long QT syndrome as an exemplar case.
Collapse
Affiliation(s)
- Adam P Hill
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, 2010, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Matthew D Perry
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, 2010, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Najah Abi-Gerges
- AnaBios Corporation, 3030 Bunker Hill St., San Diego, CA, 92109, USA
| | | | - Bernard Fermini
- Global Safety Pharmacology, Pfizer Inc, MS8274-1347 Eastern Point Road, Groton, CT, 06340, USA
| | - Jules C Hancox
- School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK
| | - Bjorn C Knollmann
- Vanderbilt University School of Medicine, 1285 Medical Research Building IV, Nashville, Tennessee, 37232, USA
| | - Gary R Mirams
- Computational Biology, Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Jon Skinner
- Cardiac Inherited Disease Group, Starship Hospital, Auckland, New Zealand
| | - Wojciech Zareba
- University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Jamie I Vandenberg
- Victor Chang Cardiac Research Institute, 405 Liverpool Street, Darlinghurst, NSW, 2010, Australia.,St. Vincent's Clinical School, University of New South Wales, Sydney, NSW, 2052, Australia
| |
Collapse
|