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Dokuchaev A, Chumarnaya T, Bazhutina A, Khamzin S, Lebedeva V, Lyubimtseva T, Zubarev S, Lebedev D, Solovyova O. Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy. Front Physiol 2023; 14:1162520. [PMID: 37497440 PMCID: PMC10367108 DOI: 10.3389/fphys.2023.1162520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/26/2023] [Indexed: 07/28/2023] Open
Abstract
Introduction: The 30-50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance DPS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient. Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance DPS was shorter in the responders. The max ML-score and DPS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and DPS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%). Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT.
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Affiliation(s)
- Arsenii Dokuchaev
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
| | - Tatiana Chumarnaya
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Anastasia Bazhutina
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
| | - Svyatoslav Khamzin
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
| | | | - Tamara Lyubimtseva
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Almazov National Medical Research Centre, Saint Petersburg, Russia
| | - Stepan Zubarev
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Almazov National Medical Research Centre, Saint Petersburg, Russia
| | - Dmitry Lebedev
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Almazov National Medical Research Centre, Saint Petersburg, Russia
| | - Olga Solovyova
- Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia
- Laboratory of Mathematical Modeling in Physiology and Medicine Based on Supercomputers, Ural Federal University, Ekaterinburg, Russia
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Biasi N, Seghetti P, Mercati M, Tognetti A. A smoothed boundary bidomain model for cardiac simulations in anatomically detailed geometries. PLoS One 2023; 18:e0286577. [PMID: 37294777 PMCID: PMC10256234 DOI: 10.1371/journal.pone.0286577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/18/2023] [Indexed: 06/11/2023] Open
Abstract
This manuscript presents a novel finite difference method to solve cardiac bidomain equations in anatomical models of the heart. The proposed method employs a smoothed boundary approach that represents the boundaries between the heart and the surrounding medium as a spatially diffuse interface of finite thickness. The bidomain boundary conditions are implicitly implemented in the smoothed boundary bidomain equations presented in the manuscript without the need of a structured mesh that explicitly tracks the heart-torso boundaries. We reported some significant examples assessing the method's accuracy using nontrivial test geometries and demonstrating the applicability of the method to complex anatomically detailed human cardiac geometries. In particular, we showed that our approach could be employed to simulate cardiac defibrillation in a human left ventricle comprising fiber architecture. The main advantage of the proposed method is the possibility of implementing bidomain boundary conditions directly on voxel structures, which makes it attractive for three dimensional, patient specific simulations based on medical images. Moreover, given the ease of implementation, we believe that the proposed method could provide an interesting and feasible alternative to finite element methods, and could find application in future cardiac research guiding electrotherapy with computational models.
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Affiliation(s)
- Niccolò Biasi
- Information Engineering Department, University of Pisa, Pisa, Italy
| | - Paolo Seghetti
- Health Science Interdisciplinary Center, Scuola Superiore Sant’Anna, Pisa, Italy
- National Research Council, Institute of Clinical Physiology, Pisa, Italy
| | - Matteo Mercati
- Information Engineering Department, University of Pisa, Pisa, Italy
| | - Alessandro Tognetti
- Information Engineering Department, University of Pisa, Pisa, Italy
- Research Centre “E. Piaggio”, University of Pisa, Pisa, Italy
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Schreiber LM, Lohr D, Baltes S, Vogel U, Elabyad IA, Bille M, Reiter T, Kosmala A, Gassenmaier T, Stefanescu MR, Kollmann A, Aures J, Schnitter F, Pali M, Ueda Y, Williams T, Christa M, Hofmann U, Bauer W, Gerull B, Zernecke A, Ergün S, Terekhov M. Ultra-high field cardiac MRI in large animals and humans for translational cardiovascular research. Front Cardiovasc Med 2023; 10:1068390. [PMID: 37255709 PMCID: PMC10225557 DOI: 10.3389/fcvm.2023.1068390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 04/04/2023] [Indexed: 06/01/2023] Open
Abstract
A key step in translational cardiovascular research is the use of large animal models to better understand normal and abnormal physiology, to test drugs or interventions, or to perform studies which would be considered unethical in human subjects. Ultrahigh field magnetic resonance imaging (UHF-MRI) at 7 T field strength is becoming increasingly available for imaging of the heart and, when compared to clinically established field strengths, promises better image quality and image information content, more precise functional analysis, potentially new image contrasts, and as all in-vivo imaging techniques, a reduction of the number of animals per study because of the possibility to scan every animal repeatedly. We present here a solution to the dual use problem of whole-body UHF-MRI systems, which are typically installed in clinical environments, to both UHF-MRI in large animals and humans. Moreover, we provide evidence that in such a research infrastructure UHF-MRI, and ideally combined with a standard small-bore UHF-MRI system, can contribute to a variety of spatial scales in translational cardiovascular research: from cardiac organoids, Zebra fish and rodent hearts to large animal models such as pigs and humans. We present pilot data from serial CINE, late gadolinium enhancement, and susceptibility weighted UHF-MRI in a myocardial infarction model over eight weeks. In 14 pigs which were delivered from a breeding facility in a national SARS-CoV-2 hotspot, we found no infection in the incoming pigs. Human scanning using CINE and phase contrast flow measurements provided good image quality of the left and right ventricle. Agreement of functional analysis between CINE and phase contrast MRI was excellent. MRI in arrested hearts or excised vascular tissue for MRI-based histologic imaging, structural imaging of myofiber and vascular smooth muscle cell architecture using high-resolution diffusion tensor imaging, and UHF-MRI for monitoring free radicals as a surrogate for MRI of reactive oxygen species in studies of oxidative stress are demonstrated. We conclude that UHF-MRI has the potential to become an important precision imaging modality in translational cardiovascular research.
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Affiliation(s)
- Laura M. Schreiber
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - David Lohr
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Steffen Baltes
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Ulrich Vogel
- Institute for Hygiene and Microbiology, University of Wuerzburg, Wuerzburg, Germany
| | - Ibrahim A. Elabyad
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Maya Bille
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Theresa Reiter
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
- Department of Internal Medicine I/Cardiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Aleksander Kosmala
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
- Department of Radiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Tobias Gassenmaier
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
- Department of Radiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Maria R. Stefanescu
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Alena Kollmann
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Julia Aures
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Florian Schnitter
- Department of Internal Medicine I/Cardiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Mihaela Pali
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
| | - Yuichiro Ueda
- Institute of Anatomy and Cell Biology, Julius-Maximilians-University, Wuerzburg, Germany
| | - Tatiana Williams
- Department of Cardiovascular Genetics, Comprehensive Heart Failure Center Wuerzburg, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Martin Christa
- Department of Internal Medicine I/Cardiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Ulrich Hofmann
- Department of Internal Medicine I/Cardiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Wolfgang Bauer
- Department of Internal Medicine I/Cardiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Brenda Gerull
- Department of Internal Medicine I/Cardiology, University Hospital Wuerzburg, Wuerzburg, Germany
- Department of Cardiovascular Genetics, Comprehensive Heart Failure Center Wuerzburg, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Alma Zernecke
- Institute of Experimental Biomedicine, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Süleyman Ergün
- Institute of Anatomy and Cell Biology, Julius-Maximilians-University, Wuerzburg, Germany
| | - Maxim Terekhov
- Department of Cardiovascular Imaging and Chair of Molecular and Cellular Imaging, Comprehensive Heart Failure Center Wuerzburg (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany
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Barone A, Grieco D, Gizzi A, Molinari L, Zaltieri M, Massaroni C, Loppini A, Schena E, Bressi E, de Ruvo E, Caló L, Filippi S. A Simulation Study of the Effects of His Bundle Pacing in Left Bundle Branch Block. Med Eng Phys 2022; 107:103847. [DOI: 10.1016/j.medengphy.2022.103847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 04/30/2022] [Accepted: 07/09/2022] [Indexed: 11/28/2022]
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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.
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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
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An Automata-Based Cardiac Electrophysiology Simulator to Assess Arrhythmia Inducibility. MATHEMATICS 2022. [DOI: 10.3390/math10081293] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Personalized cardiac electrophysiology simulations have demonstrated great potential to study cardiac arrhythmias and help in therapy planning of radio-frequency ablation. Its application to analyze vulnerability to ventricular tachycardia and sudden cardiac death in infarcted patients has been recently explored. However, the detailed multi-scale biophysical simulations used in these studies are very demanding in terms of memory and computational resources, which prevents their clinical translation. In this work, we present a fast phenomenological system based on cellular automata (CA) to simulate personalized cardiac electrophysiology. The system is trained on biophysical simulations to reproduce cellular and tissue dynamics in healthy and pathological conditions, including action potential restitution, conduction velocity restitution and cell safety factor. We show that a full ventricular simulation can be performed in the order of seconds, emulate the results of a biophysical simulation and reproduce a patient’s ventricular tachycardia in a model that includes a heterogeneous scar region. The system could be used to study the risk of arrhythmia in infarcted patients for a large number of scenarios.
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