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Jaffery OA, Melki L, Slabaugh G, Good WW, Roney CH. A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data. Arrhythm Electrophysiol Rev 2024; 13:e08. [PMID: 38807744 PMCID: PMC11131150 DOI: 10.15420/aer.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/27/2023] [Indexed: 05/30/2024] Open
Abstract
Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.
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Affiliation(s)
- Ovais A Jaffery
- School of Engineering and Materials Science, Queen Mary University of London London, UK
| | - Lea Melki
- R&D Algorithms, Acutus Medical Carlsbad, CA, US
| | - Gregory Slabaugh
- Digital Environment Research Institute, Queen Mary University of London London, UK
| | | | - Caroline H Roney
- School of Engineering and Materials Science, Queen Mary University of London London, UK
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2
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Shariat MH, Neira V, Redfearn DP. Sequential Intracardiac Activation Time Mapping of Arrhythmias Without Fiducial Time References. IEEE Trans Biomed Eng 2024; 71:1478-1487. [PMID: 38060362 DOI: 10.1109/tbme.2023.3340524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Sequential local activation time (LAT) mapping of intracardiac electrograms' activations requires a stable reference signal to align recording phases. OBJECTIVE This work's purpose is to develop an LAT mapping approach that does not rely on a time-alignment reference (TAR). METHODS To create an LAT map in absence of TAR (TARLess), the coordinates and LATs of recording electrodes are collected sequentially; a bank of candidate functions (CFs) is constructed that contains constant binary level CFs and non-linear functions of recording points' coordinates. Finally, a subset of CFs is linearly combined to create an activation time function with output matching electrodes' LATs. Synthetic and clinical data were deployed to validate TARLess. A simple two-dimensional computer model was used to create 30 different wavefront collision scenarios in a region with spatial conduction heterogeneities. Furthermore, sequential recordings were collected from seven atrial fibrillation patients during stimulation from one or two sites, after sinus rhythm was achieved post catheter ablation. RESULTS We showed that TARLess maps are similar to the one that uses TAR; for the 20 clinical maps, the mean absolute difference between measured LAT with the TAR and TARLess approach was 5.2 ±2.0 milliseconds. CONCLUSION We developed a novel method to create an LAT map of sequential recordings without using any TAR and showed that it can create accurate maps even during the collision of multiple wavefronts. SIGNIFICANCE TARLess mapping does not require a reference catheter which could lead to reduction in ablation procedure duration, cost, and potential complications.
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Invers-Rubio E, Hernández-Romero I, Reventos-Presmanes J, Ferro E, Guichard JB, Regany-Closa M, Pellicer-Sendra B, Borras R, Prat-Gonzalez S, Tolosana JM, Porta-Sanchez A, Arbelo E, Guasch E, Sitges M, Brugada J, Guillem MS, Roca-Luque I, Climent AM, Mont L, Althoff TF. Regional conduction velocities determined by noninvasive mapping are associated with arrhythmia-free survival after atrial fibrillation ablation. Heart Rhythm 2024:S1547-5271(24)02390-7. [PMID: 38636930 DOI: 10.1016/j.hrthm.2024.04.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/24/2024] [Accepted: 04/12/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Atrial arrhythmogenic substrate is a key determinant of atrial fibrillation (AF) recurrence after pulmonary vein isolation (PVI), and reduced conduction velocities have been linked to adverse outcome. However, a noninvasive method to assess such electrophysiologic substrate is not available to date. OBJECTIVE This study aimed to noninvasively assess regional conduction velocities and their association with arrhythmia-free survival after PVI. METHODS A consecutive 52 patients scheduled for AF ablation (PVI only) and 19 healthy controls were prospectively included and received electrocardiographic imaging (ECGi) to noninvasively determine regional atrial conduction velocities in sinus rhythm. A novel ECGi technology obviating the need of additional computed tomography or cardiac magnetic resonance imaging was applied and validated by invasive mapping. RESULTS Mean ECGi-determined atrial conduction velocities were significantly lower in AF patients than in healthy controls (1.45 ± 0.15 m/s vs 1.64 ± 0.15 m/s; P < .0001). Differences were particularly pronounced in a regional analysis considering only the segment with the lowest average conduction velocity in each patient (0.8 ± 0.22 m/s vs 1.08 ± 0.26 m/s; P < .0001). This average conduction velocity of the "slowest" segment was independently associated with arrhythmia recurrence and better discriminated between PVI responders and nonresponders than previously proposed predictors, including left atrial size and late gadolinium enhancement (magnetic resonance imaging). Patients without slow-conduction areas (mean conduction velocity <0.78 m/s) showed significantly higher 12-month arrhythmia-free survival than those with 1 or more slow-conduction areas (88.9% vs 48.0%; P = .002). CONCLUSION This is the first study to investigate regional atrial conduction velocities noninvasively. The absence of ECGi-determined slow-conduction areas well discriminates PVI responders from nonresponders. Such noninvasive assessment of electrical arrhythmogenic substrate may guide treatment strategies and be a step toward personalized AF therapy.
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Affiliation(s)
- Eric Invers-Rubio
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
| | | | - Jana Reventos-Presmanes
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; ITACA Institute, Universitat Politècnica de València, València, Spain
| | - Elisenda Ferro
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Jean-Baptiste Guichard
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Department of Cardiology, University Hospital of Saint-Étienne, Saint-Étienne, France
| | - Mariona Regany-Closa
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Berta Pellicer-Sendra
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Roger Borras
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Susanna Prat-Gonzalez
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
| | - Jose Maria Tolosana
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV), Madrid, Spain
| | - Andreu Porta-Sanchez
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV), Madrid, Spain
| | - Elena Arbelo
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV), Madrid, Spain
| | - Eduard Guasch
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV), Madrid, Spain
| | - Marta Sitges
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV), Madrid, Spain
| | - Josep Brugada
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV), Madrid, Spain
| | - Maria S Guillem
- ITACA Institute, Universitat Politècnica de València, València, Spain
| | - Ivo Roca-Luque
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV), Madrid, Spain
| | - Andreu M Climent
- ITACA Institute, Universitat Politècnica de València, València, Spain
| | - Lluís Mont
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain; Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV), Madrid, Spain
| | - Till F Althoff
- Department of Cardiology, Hospital Clinic Cardiovascular Institute (ICCV), Universitat de Barcelona, Barcelona, Catalonia, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain.
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Saha S, Linz D, Saha D, McEwan A, Baumert M. Overcoming Uncertainties in Electrogram-Based Atrial Fibrillation Mapping: A Review. Cardiovasc Eng Technol 2024; 15:52-64. [PMID: 37962813 DOI: 10.1007/s13239-023-00696-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023]
Abstract
In clinical rhythmology, intracardiac bipolar electrograms (EGMs) play a critical role in investigating the triggers and substrates inducing and perpetuating atrial fibrillation (AF). However, the interpretation of bipolar EGMs is ambiguous due to several aspects of electrodes, mapping algorithms and wave propagation dynamics, so it requires several variables to describe the effects of these uncertainties on EGM analysis. In this narrative review, we critically evaluate the potential impact of such uncertainties on the design of cardiac mapping tools on AF-related substrate characterization. Literature suggest uncertainties are due to several variables, including the wave propagation vector, the wave's incidence angle, inter-electrode spacing, electrode size and shape, and tissue contact. The preprocessing of the EGM signals and mapping density will impact the electro-anatomical representation and the features extracted from the local electrical activities. The superposition of multiple waves further complicates EGM interpretation. The inclusion of these uncertainties is a nontrivial problem but their consideration will yield a better interpretation of the intra-atrial dynamics in local activation patterns. From a translational perspective, this review provides a concise but complete overview of the critical variables for developing more precise cardiac mapping tools.
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Affiliation(s)
- Simanto Saha
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, 2008, Australia.
| | - Dominik Linz
- Centre for Heart Rhythm Disorders, The University of Adelaide, Adelaide, SA, 5000, Australia
| | - Dyuti Saha
- Kumudini Women's Medical College, The University of Dhaka, Tangail, 1940, Dhaka, Bangladesh
| | - Alistair McEwan
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, 2008, Australia
| | - Mathias Baumert
- School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide, SA, 5000, Australia
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5
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Pancorbo L, Ruipérez-Campillo S, Tormos Á, Guill A, Cervigón R, Alberola A, Chorro FJ, Millet J, Castells F. Vector Field Heterogeneity for the Assessment of Locally Disorganised Cardiac Electrical Propagation Wavefronts From High-Density Multielectrodes. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 5:32-44. [PMID: 38445238 PMCID: PMC10914212 DOI: 10.1109/ojemb.2023.3344349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/22/2023] [Accepted: 11/28/2023] [Indexed: 03/07/2024] Open
Abstract
High-density multielectrode catheters are becoming increasingly popular in cardiac electrophysiology for advanced characterisation of the cardiac tissue, due to their potential to identify impaired sites. These are often characterised by abnormal electrical conduction, which may cause locally disorganised propagation wavefronts. To quantify it, a novel heterogeneity parameter based on vector field analysis is proposed, utilising finite differences to measure direction changes between adjacent cliques. The proposed Vector Field Heterogeneity metric has been evaluated on a set of simulations with controlled levels of organisation in vector maps, and a variety of grid sizes. Furthermore, it has been tested on animal experimental models of isolated Langendorff-perfused rabbit hearts. The proposed parameter exhibited superior capturing ability of heterogeneous propagation wavefronts compared to the classical Spatial Inhomogeneity Index, and simulations proved that the metric effectively captures gradual increments in disorganisation in propagation patterns. Notably, it yielded robust and consistent outcomes for [Formula: see text] grid sizes, underscoring its suitability for the latest generation of orientation-independent cardiac catheters.
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Affiliation(s)
- Lucía Pancorbo
- ITACA InstituteUniversitat Politècnica de València46022ValenciaSpain
| | | | - Álvaro Tormos
- ITACA InstituteUniversitat Politècnica de València46022ValenciaSpain
| | - Antonio Guill
- ITACA InstituteUniversitat Politècnica de València46022ValenciaSpain
| | | | - Antonio Alberola
- Departamento de FisiologíaUniversidad de València46010ValenciaSpain
- Instituto de Investigación INCLIVA46010ValenciaSpain
- CIBER E. Cardiovasculares28029MadridSpain
| | - Francisco Javier Chorro
- CIBER E. Cardiovasculares28029MadridSpain
- Departamento de MedicinaUniversidad de València46010ValenciaSpain
- Instituto de Investigación INCLIVA46010ValenciaSpain
- Servicio de CardiologíaHospital Clínic Universitari de València46010ValenciaSpain
| | - José Millet
- ITACA InstituteUniversitat Politècnica de València46022ValenciaSpain
- Centro de Investigación Biomédica en Red Enfermedades Cardiovascular28029MadridSpain
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6
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Roney CH, Solis Lemus JA, Lopez Barrera C, Zolotarev A, Ulgen O, Kerfoot E, Bevis L, Misghina S, Vidal Horrach C, Jaffery OA, Ehnesh M, Rodero C, Dharmaprani D, Ríos-Muñoz GR, Ganesan A, Good WW, Neic A, Plank G, Hopman LHGA, Götte MJW, Honarbakhsh S, Narayan SM, Vigmond E, Niederer S. Constructing bilayer and volumetric atrial models at scale. Interface Focus 2023; 13:20230038. [PMID: 38106921 PMCID: PMC10722212 DOI: 10.1098/rsfs.2023.0038] [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: 08/10/2023] [Accepted: 11/15/2023] [Indexed: 12/19/2023] Open
Abstract
To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale (https://github.com/pcmlab/atrialmtk).
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Affiliation(s)
- Caroline H. Roney
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Jose Alonso Solis Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Carlos Lopez Barrera
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
- Center for Research in Advanced Materials S.C (CIMAV), Chihuahua, Mexico
| | - Alexander Zolotarev
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Onur Ulgen
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Eric Kerfoot
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
| | - Laura Bevis
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Semhar Misghina
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Caterina Vidal Horrach
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Ovais A. Jaffery
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Mahmoud Ehnesh
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Cristobal Rodero
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Dhani Dharmaprani
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Gonzalo R. Ríos-Muñoz
- Bioengineering Department, Universidad Carlos III de Madrid, Madrid 28911, Spain
- Department of Cardiology, Gregorio Marañón Health Research Institute (IiSGM), Hospital General Universitario Gregorio Marañón, Madrid 28007, Spain
- Center for Biomedical Research in Cardiovascular Disease Network (CIBERCV), Madrid 28029, Spain
| | - Anand Ganesan
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | | | | | - Gernot Plank
- Gottfried Schatz Research Center-Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | | | | | - Shohreh Honarbakhsh
- Electrophysiology Department, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Sanjiv M. Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Palo Alto, CA, USA
| | - Edward Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France
- IMB, UMR 5251, University Bordeaux, Talence 33400, France
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
- Turing Research and Innovation Cluster in Digital Twins (TRIC: DT), The Alan Turing Institute, London, UK
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7
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Azzolin L, Eichenlaub M, Nagel C, Nairn D, Sánchez J, Unger L, Arentz T, Westermann D, Dössel O, Jadidi A, Loewe A. AugmentA: Patient-specific augmented atrial model generation tool. Comput Med Imaging Graph 2023; 108:102265. [PMID: 37392493 DOI: 10.1016/j.compmedimag.2023.102265] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/07/2023] [Accepted: 06/03/2023] [Indexed: 07/03/2023]
Abstract
Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.
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Affiliation(s)
- Luca Azzolin
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany.
| | - Martin Eichenlaub
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Claudia Nagel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Deborah Nairn
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jorge Sánchez
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Laura Unger
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Thomas Arentz
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Dirk Westermann
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Amir Jadidi
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
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8
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Bifulco SF, Macheret F, Scott GD, Akoum N, Boyle PM. Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models. J Am Heart Assoc 2023; 12:e030500. [PMID: 37581387 PMCID: PMC10492949 DOI: 10.1161/jaha.123.030500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/21/2023] [Indexed: 08/16/2023]
Abstract
Background Postablation arrhythmia recurrence occurs in ~40% of patients with persistent atrial fibrillation. Fibrotic remodeling exacerbates arrhythmic activity in persistent atrial fibrillation and can play a key role in reentrant arrhythmia, but emergent interaction between nonconductive ablation-induced scar and native fibrosis (ie, residual fibrosis) is poorly understood. Methods and Results We conducted computational simulations in pre- and postablation left atrial models reconstructed from late gadolinium enhanced magnetic resonance imaging scans to test the hypothesis that ablation in patients with persistent atrial fibrillation creates new substrate conducive to recurrent arrhythmia mediated by anchored reentry. We trained a random forest machine learning classifier to accurately pinpoint specific nonconductive tissue regions (ie, areas of ablation-delivered scar or vein/valve boundaries) with the capacity to serve as substrate for anchored reentry-driven recurrent arrhythmia (area under the curve: 0.91±0.03). Our analysis suggests there is a distinctive nonconductive tissue pattern prone to serving as arrhythmogenic substrate in postablation models, defined by a specific size and proximity to residual fibrosis. Conclusions Overall, this suggests persistent atrial fibrillation ablation transforms substrate that favors functional reentry (ie, rotors meandering in excitable tissue) into an arrhythmogenic milieu more conducive to anchored reentry. Our work also indicates that explainable machine learning and computational simulations can be combined to effectively probe mechanisms of recurrent arrhythmia.
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Affiliation(s)
| | - Fima Macheret
- Division of CardiologyUniversity of WashingtonSeattleWAUSA
| | - Griffin D. Scott
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
| | - Nazem Akoum
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
- Division of CardiologyUniversity of WashingtonSeattleWAUSA
| | - Patrick M. Boyle
- Department of BioengineeringUniversity of WashingtonSeattleWAUSA
- Institute for Stem Cell and Regenerative MedicineUniversity of WashingtonSeattleWAUSA
- Center for Cardiovascular BiologyUniversity of WashingtonSeattleWAUSA
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9
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He J, Pertsov AM, Cherry EM, Fenton FH, Roney CH, Niederer SA, Zang Z, Mangharam R. Fiber Organization has Little Effect on Electrical Activation Patterns during Focal Arrhythmias in the Left Atrium. ARXIV 2023:arXiv:2210.16497v3. [PMID: 36776816 PMCID: PMC9915751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Over the past two decades there has been a steady trend towards the development of realistic models of cardiac conduction with increasing levels of detail. However, making models more realistic complicates their personalization and use in clinical practice due to limited availability of tissue and cellular scale data. One such limitation is obtaining information about myocardial fiber organization in the clinical setting. In this study, we investigated a chimeric model of the left atrium utilizing clinically derived patient-specific atrial geometry and a realistic, yet foreign for a given patient fiber organization. We discovered that even significant variability of fiber organization had a relatively small effect on the spatio-temporal activation pattern during regular pacing. For a given pacing site, the activation maps were very similar across all fiber organizations tested.
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Affiliation(s)
- Jiyue He
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
| | | | - Elizabeth M Cherry
- School of Computational Science and Engineering, Georgia Institute of Technology, USA
| | | | - Caroline H Roney
- School of Engineering and Materials Science, Queen Mary University of London, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, UK
| | - Zirui Zang
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
| | - Rahul Mangharam
- Department of Electrical and Systems Engineering, University of Pennsylvania, USA
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10
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Calibrating cardiac electrophysiology models using latent Gaussian processes on atrial manifolds. Sci Rep 2022; 12:16572. [PMID: 36195766 PMCID: PMC9532401 DOI: 10.1038/s41598-022-20745-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/19/2022] [Indexed: 11/24/2022] Open
Abstract
Models of electrical excitation and recovery in the heart have become increasingly detailed, but have yet to be used routinely in the clinical setting to guide personalized intervention in patients. One of the main challenges is calibrating models from the limited measurements that can be made in a patient during a standard clinical procedure. In this work, we propose a novel framework for the probabilistic calibration of electrophysiology parameters on the left atrium of the heart using local measurements of cardiac excitability. Parameter fields are represented as Gaussian processes on manifolds and are linked to measurements via surrogate functions that map from local parameter values to measurements. The posterior distribution of parameter fields is then obtained. We show that our method can recover parameter fields used to generate localised synthetic measurements of effective refractory period. Our methodology is applicable to other measurement types collected with clinical protocols, and more generally for calibration where model parameters vary over a manifold.
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11
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Santurri M, Bonga J, Schmid M, Cauti FM, Solimene F, Polselli M, Bura M, Piccolo F, Malacrida M, Pelargonio G, Spera FR, Bianchi S, Rossi P. Automated conduction velocity estimation based on isochronal activation of heart chambers. J Interv Card Electrophysiol 2022; 66:647-660. [PMID: 36178554 PMCID: PMC10066170 DOI: 10.1007/s10840-022-01339-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 08/09/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND Spatial differences in conduction velocity (CV) are critical for cardiac arrhythmias induction. We propose a method for an automated CV calculation to identify areas of slower conduction during cardiac arrhythmias and sinus rhythm. METHODS Color-coded representations of the isochronal activation map using data coming from the RHYTHMIA™ Mapping System were reproduced by applying a temporal isochronal window at 20 ms. Geodesic distances of the 3D mesh were calculated using an algorithm selecting the minimum distance pathway (MDP). The CV estimation was performed considering points on the boundary of two spatially and temporally adjacent isochrones. For each of the boundary points of a given isochrone, the nearest boundary point of the consecutive isochrone was chosen, the MDP was evaluated, and a map of CV was created. The proposed method has been applied to a population of 29 patients. RESULTS In all cases of perimitral atrial flutter (16 pts out of 29 (55%)), areas with significantly low CV (< 30 cm/s) were found. Half of the cases present regions with low CV located in the anterior wall. No case with low CV at the so-called LA isthmus was observed. Right atrial maps during common atrial flutters showed low CV areas mainly located in the inferior inter-atrial septum. No areas of low CV were observed in subjects without a history of atrial arrhythmia while pts affected by paroxysmal AF showed areas with a limited extension of low CV. CONCLUSIONS The proposed software for automated CV estimation allows the identification of low CV areas, potentially helping electrophysiologists to plan the ablation strategy.
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Affiliation(s)
- Michela Santurri
- BioLab3, Biomedical Engineering Laboratory, Roma Tre University, Rome, Italy
| | - Jennifer Bonga
- BioLab3, Biomedical Engineering Laboratory, Roma Tre University, Rome, Italy
| | - Maurizio Schmid
- BioLab3, Biomedical Engineering Laboratory, Roma Tre University, Rome, Italy
| | - Filippo Maria Cauti
- Arrhythmology Unit, Hospital Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy
| | - Francesco Solimene
- Electrophysiology Unit, Clinica Montevergine, Mercogliano, Avellino, Italy
| | - Marco Polselli
- Arrhythmology Unit, Hospital Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy
| | | | | | | | - Gemma Pelargonio
- Cardiovascular Sciences Department, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Francesco Raffaele Spera
- Cardiovascular Sciences Department, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Stefano Bianchi
- Arrhythmology Unit, Hospital Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy
| | - Pietro Rossi
- Arrhythmology Unit, Hospital Fatebenefratelli Isola Tiberina-Gemelli Isola, Rome, Italy.
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12
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Atrial conduction velocity mapping: clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate. Med Biol Eng Comput 2022; 60:2463-2478. [PMID: 35867323 PMCID: PMC9365755 DOI: 10.1007/s11517-022-02621-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/07/2022] [Indexed: 11/02/2022]
Abstract
Characterizing patient-specific atrial conduction properties is important for understanding arrhythmia drivers, for predicting potential arrhythmia pathways, and for personalising treatment approaches. One metric that characterizes the health of the myocardial substrate is atrial conduction velocity, which describes the speed and direction of propagation of the electrical wavefront through the myocardium. Atrial conduction velocity mapping algorithms are under continuous development in research laboratories and in industry. In this review article, we give a broad overview of different categories of currently published methods for calculating CV, and give insight into their different advantages and disadvantages overall. We classify techniques into local, global, and inverse methods, and discuss these techniques with respect to their faithfulness to the biophysics, incorporation of uncertainty quantification, and their ability to take account of the atrial manifold.
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13
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Siles-Paredes JG, Crowley CJ, Fenton FH, Bhatia N, Iravanian S, Sandoval I, Pollnow S, Dössel O, Salinet J, Uzelac I. Circle Method for Robust Estimation of Local Conduction Velocity High-Density Maps From Optical Mapping Data: Characterization of Radiofrequency Ablation Sites. Front Physiol 2022; 13:794761. [PMID: 36035466 PMCID: PMC9417315 DOI: 10.3389/fphys.2022.794761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 06/15/2022] [Indexed: 01/10/2023] Open
Abstract
Conduction velocity (CV) slowing is associated with atrial fibrillation (AF) and reentrant ventricular tachycardia (VT). Clinical electroanatomical mapping systems used to localize AF or VT sources as ablation targets remain limited by the number of measuring electrodes and signal processing methods to generate high-density local activation time (LAT) and CV maps of heterogeneous atrial or trabeculated ventricular endocardium. The morphology and amplitude of bipolar electrograms depend on the direction of propagating electrical wavefront, making identification of low-amplitude signal sources commonly associated with fibrotic area difficulty. In comparison, unipolar electrograms are not sensitive to wavefront direction, but measurements are susceptible to distal activity. This study proposes a method for local CV calculation from optical mapping measurements, termed the circle method (CM). The local CV is obtained as a weighted sum of CV values calculated along different chords spanning a circle of predefined radius centered at a CV measurement location. As a distinct maximum in LAT differences is along the chord normal to the propagating wavefront, the method is adaptive to the propagating wavefront direction changes, suitable for electrical conductivity characterization of heterogeneous myocardium. In numerical simulations, CM was validated characterizing modeled ablated areas as zones of distinct CV slowing. Experimentally, CM was used to characterize lesions created by radiofrequency ablation (RFA) on isolated hearts of rats, guinea pig, and explanted human hearts. To infer the depth of RFA-created lesions, excitation light bands of different penetration depths were used, and a beat-to-beat CV difference analysis was performed to identify CV alternans. Despite being limited to laboratory research, studies based on CM with optical mapping may lead to new translational insights into better-guided ablation therapies.
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Affiliation(s)
- Jimena G. Siles-Paredes
- Graduate Program in Biotechnoscience, Federal University of ABC, São Paulo, Brazil
- HEartLab, Federal University of ABC, São Paulo, Brazil
- *Correspondence: Jimena G. Siles-Paredes,
| | | | - Flavio H. Fenton
- Georgia Institute of Technology, School of Physics, Atlanta, GA, United States
| | - Neal Bhatia
- Division of Cardiology, Section of Electrophysiology, Emory University Hospital, Atlanta, GA, United States
| | - Shahriar Iravanian
- Division of Cardiology, Section of Electrophysiology, Emory University Hospital, Atlanta, GA, United States
| | | | - Stefan Pollnow
- Karlsruhe Institute of Technology (KIT)/Institute of Biomedical Engineering, Karlsruhe, Germany
| | - Olaf Dössel
- Karlsruhe Institute of Technology (KIT)/Institute of Biomedical Engineering, Karlsruhe, Germany
| | - João Salinet
- Graduate Program in Biotechnoscience, Federal University of ABC, São Paulo, Brazil
- HEartLab, Federal University of ABC, São Paulo, Brazil
| | - Ilija Uzelac
- Georgia Institute of Technology, School of Physics, Atlanta, GA, United States
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14
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Hellar J, Cosentino R, John MM, Post A, Buchan S, Razavi M, Aazhang B. Manifold Approximating Graph Interpolation of Cardiac Local Activation Time. IEEE Trans Biomed Eng 2022; 69:3253-3264. [PMID: 35404808 PMCID: PMC9549513 DOI: 10.1109/tbme.2022.3166447] [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] [Indexed: 11/26/2022]
Abstract
Objective: Local activation time (LAT) mapping of cardiac chambers is vital for targeted treatment of cardiac arrhythmias in catheter ablation procedures. Current methods require too many LAT observations for an accurate interpolation of the necessarily sparse LAT signal extracted from intracardiac electrograms (EGMs). Additionally, conventional performance metrics for LAT interpolation algorithms do not accurately measure the quality of interpolated maps. We propose, first, a novel method for spatial interpolation of the LAT signal which requires relatively few observations; second, a realistic sub-sampling protocol for LAT interpolation testing; and third, a new color-based metric for evaluation of interpolation quality that quantifies perceived differences in LAT maps. Methods: We utilize a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem on the manifold with a closed-form solution. The metric proposed uses a color difference equation and color theory to quantify visual differences in generated LAT maps. Results: We evaluate our approach on a dataset consisting of seven LAT maps from four patients obtained by the CARTO electroanatomic mapping system during premature ventricular complex (PVC) ablation procedures. Random sub-sampling and re-interpolation of the LAT observations show excellent accuracy for relatively few observations, achieving on average 6% lower error than state-of-the-art techniques for only 100 observations. Conclusion: Our study suggests that graph signal processing methods can improve LAT mapping for cardiac ablation procedures. Significance: The proposed method can reduce patient time in surgery by decreasing the number of LAT observations needed for an accurate LAT map.
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15
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Gander L, Pezzuto S, Gharaviri A, Krause R, Perdikaris P, Sahli Costabal F. Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification. Front Physiol 2022; 13:757159. [PMID: 35330935 PMCID: PMC8940533 DOI: 10.3389/fphys.2022.757159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolution models to explore the atrial surface and combine seamlessly with high-resolution models to identify regions of inducibility. We test our methodology in 9 different cases, with different levels of fibrosis and ablation treatments, totalling 1,800 high resolution and 900 low resolution simulations of atrial fibrillation. When trained with 40 samples, our multi-fidelity classifier that combines low and high resolution models, shows a balanced accuracy that is, on average, 5.7% higher than a nearest neighbor classifier. We hope that this new technique will allow faster and more precise clinical applications of computational models for atrial fibrillation. All data and code accompanying this manuscript will be made publicly available at: https://github.com/fsahli/AtrialMFclass.
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Affiliation(s)
- Lia Gander
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Ali Gharaviri
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Paris Perdikaris
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States
| | - Francisco Sahli Costabal
- Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.,Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile
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16
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Masè M, Cristoforetti A, Del Greco M, Ravelli F. A Divergence-Based Approach for the Identification of Atrial Fibrillation Focal Drivers From Multipolar Mapping: A Computational Study. Front Physiol 2021; 12:749430. [PMID: 35002755 PMCID: PMC8740027 DOI: 10.3389/fphys.2021.749430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
The expanding role of catheter ablation of atrial fibrillation (AF) has stimulated the development of novel mapping strategies to guide the procedure. We introduce a novel approach to characterize wave propagation and identify AF focal drivers from multipolar mapping data. The method reconstructs continuous activation patterns in the mapping area by a radial basis function (RBF) interpolation of multisite activation time series. Velocity vector fields are analytically determined, and the vector field divergence is used as a marker of focal drivers. The method was validated in a tissue patch cellular automaton model and in an anatomically realistic left atrial (LA) model with Courtemanche-Ramirez-Nattel ionic dynamics. Divergence analysis was effective in identifying focal drivers in a complex simulated AF pattern. Localization was reliable even with consistent reduction (47%) in the number of mapping points and in the presence of activation time misdetections (noise <10% of the cycle length). Proof-of-concept application of the method to human AF mapping data showed that divergence analysis consistently detected focal activation in the pulmonary veins and LA appendage area. These results suggest the potential of divergence analysis in combination with multipolar mapping to identify AF critical sites. Further studies on large clinical datasets may help to assess the clinical feasibility and benefit of divergence analysis for the optimization of ablation treatment.
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Affiliation(s)
- Michela Masè
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology – CIBIO, University of Trento, Trento, Italy
- Institute of Mountain Emergency Medicine, EURAC Research, Bolzano, Italy
| | - Alessandro Cristoforetti
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology – CIBIO, University of Trento, Trento, Italy
| | - Maurizio Del Greco
- Division of Cardiology, Santa Maria del Carmine Hospital, Rovereto, Italy
| | - Flavia Ravelli
- Laboratory of Biophysics and Translational Cardiology, Department of Cellular, Computational and Integrative Biology – CIBIO, University of Trento, Trento, Italy
- CISMed – Centre for Medical Sciences, University of Trento, Trento, Italy
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17
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Han B, Trew ML, Zgierski-Johnston CM. Cardiac Conduction Velocity, Remodeling and Arrhythmogenesis. Cells 2021; 10:cells10112923. [PMID: 34831145 PMCID: PMC8616078 DOI: 10.3390/cells10112923] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 10/14/2021] [Accepted: 10/22/2021] [Indexed: 02/06/2023] Open
Abstract
Cardiac electrophysiological disorders, in particular arrhythmias, are a key cause of morbidity and mortality throughout the world. There are two basic requirements for arrhythmogenesis: an underlying substrate and a trigger. Altered conduction velocity (CV) provides a key substrate for arrhythmogenesis, with slowed CV increasing the probability of re-entrant arrhythmias by reducing the length scale over which re-entry can occur. In this review, we examine methods to measure cardiac CV in vivo and ex vivo, discuss underlying determinants of CV, and address how pathological variations alter CV, potentially increasing arrhythmogenic risk. Finally, we will highlight future directions both for methodologies to measure CV and for possible treatments to restore normal CV.
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Affiliation(s)
- Bo Han
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg-Bad Krozingen, 79110 Freiburg im Breisgau, Germany;
- Faculty of Medicine, University of Freiburg, 79110 Freiburg im Breisgau, Germany
- Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, 79104 Freiburg im Breisgau, Germany
- Department of Cardiovascular Surgery, The Fourth People’s Hospital of Jinan, 250031 Jinan, China
| | - Mark L. Trew
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand;
| | - Callum M. Zgierski-Johnston
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg-Bad Krozingen, 79110 Freiburg im Breisgau, Germany;
- Faculty of Medicine, University of Freiburg, 79110 Freiburg im Breisgau, Germany
- Correspondence:
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18
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Lubrecht JM, Grandits T, Gharaviri A, Schotten U, Pock T, Plank G, Krause R, Auricchio A, Conte G, Pezzuto S. Automatic reconstruction of the left atrium activation from sparse intracardiac contact recordings by inverse estimate of fibre structure and anisotropic conduction in a patient-specific model. Europace 2021; 23:i63-i70. [PMID: 33751078 DOI: 10.1093/europace/euaa392] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 12/07/2020] [Indexed: 11/14/2022] Open
Abstract
AIMS Electric conduction in the atria is direction-dependent, being faster in fibre direction, and possibly heterogeneous due to structural remodelling. Intracardiac recordings of atrial activation may convey such information, but only with high-quality data. The aim of this study was to apply a patient-specific approach to enable such assessment even when data are scarce, noisy, and incomplete. METHODS AND RESULTS Contact intracardiac recordings in the left atrium from nine patients who underwent ablation therapy were collected before pulmonary veins isolation and retrospectively included in the study. The Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps (PIEMAP), previously developed, has been used to reconstruct the conductivity tensor from sparse recordings of the activation. Regional fibre direction and conduction velocity were estimated from the fitted conductivity tensor and extensively cross-validated by clustered and sparse data removal. Electrical conductivity was successfully reconstructed in all patients. Cross-validation with respect to the measurements was excellent in seven patients (Pearson correlation r > 0.93) and modest in two patients (r = 0.62 and r = 0.74). Bland-Altman analysis showed a neglectable bias with respect to the measurements and the limit-of-agreement at -22.2 and 23.0 ms. Conduction velocity in the fibre direction was 82 ± 25 cm/s, whereas cross-fibre velocity was 46 ± 7 cm/s. Anisotropic ratio was 1.91±0.16. No significant inter-patient variability was observed. Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps correctly predicted activation times in late regions in all patients (r = 0.88) and was robust to a sparser dataset (r = 0.95). CONCLUSION Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps offers a novel approach to extrapolate the activation in unmapped regions and to assess conduction properties of the atria. It could be seamlessly integrated into existing electro-anatomic mapping systems. Personalized Inverse Eikonal model from cardiac Electro-Anatomical Maps also enables personalization of cardiac electrophysiology models.
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Affiliation(s)
- Jolijn M Lubrecht
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.,Department of Physiology, CARIM, Maastricht University, Maastricht, The Netherlands
| | - Thomas Grandits
- Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.,BioTechMed Graz, Graz, Austria
| | - Ali Gharaviri
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Ulrich Schotten
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Thomas Pock
- Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.,BioTechMed Graz, Graz, Austria
| | - Gernot Plank
- BioTechMed Graz, Graz, Austria.,Institute of Biophysics, Medical University of Graz, Graz, Austria
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Angelo Auricchio
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.,Division of Cardiology, Fondazione Cardiocentro Ticino, Lugano, Switzerland
| | - Giulio Conte
- Division of Cardiology, Fondazione Cardiocentro Ticino, Lugano, Switzerland
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
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19
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Pagani S, Dede' L, Frontera A, Salvador M, Limite LR, Manzoni A, Lipartiti F, Tsitsinakis G, Hadjis A, Della Bella P, Quarteroni A. A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation. Front Physiol 2021; 12:673612. [PMID: 34305637 PMCID: PMC8297688 DOI: 10.3389/fphys.2021.673612] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Accepted: 05/28/2021] [Indexed: 12/19/2022] Open
Abstract
In the context of cardiac electrophysiology, we propose a novel computational approach to highlight and explain the long-debated mechanisms behind atrial fibrillation (AF) and to reliably numerically predict its induction and sustainment. A key role is played, in this respect, by a new way of setting a parametrization of electrophysiological mathematical models based on conduction velocities; these latter are estimated from high-density mapping data, which provide a detailed characterization of patients' electrophysiological substrate during sinus rhythm. We integrate numerically approximated conduction velocities into a mathematical model consisting of a coupled system of partial and ordinary differential equations, formed by the monodomain equation and the Courtemanche-Ramirez-Nattel model. Our new model parametrization is then adopted to predict the formation and self-sustainment of localized reentries characterizing atrial fibrillation, by numerically simulating the onset of ectopic beats from the pulmonary veins. We investigate the paroxysmal and the persistent form of AF starting from electro-anatomical maps of two patients. The model's response to stimulation shows how substrate characteristics play a key role in inducing and sustaining these arrhythmias. Localized reentries are less frequent and less stable in case of paroxysmal AF, while they tend to anchor themselves in areas affected by severe slow conduction in case of persistent AF.
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Affiliation(s)
- S Pagani
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - L Dede'
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - A Frontera
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - M Salvador
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - L R Limite
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - A Manzoni
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - F Lipartiti
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - G Tsitsinakis
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - A Hadjis
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - P Della Bella
- Department of Arrhythmology, San Raffaele Hospital, Milan, Italy
| | - A Quarteroni
- MOX-Department of Mathematics, Politecnico di Milano, Milan, Italy.,Institute of Mathematics, EPFL, Lausanne, Switzerland
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20
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Grandits T, Pezzuto S, Costabal FS, Perdikaris P, Pock T, Plank G, Krause R. Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks. FUNCTIONAL IMAGING AND MODELING OF THE HEART : ... INTERNATIONAL WORKSHOP, FIMH ..., PROCEEDINGS. FIMH 2021; 2021:650-658. [PMID: 35098259 PMCID: PMC7612271 DOI: 10.1007/978-3-030-78710-3_62] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases and it outperforms a state of the art method in the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks.
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Affiliation(s)
- Thomas Grandits
- Institute of Computer Graphics and Vision, TU Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Francisco Sahli Costabal
- Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance
| | - Paris Perdikaris
- Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Thomas Pock
- Institute of Computer Graphics and Vision, TU Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Gernot Plank
- BioTechMed-Graz, Graz, Austria
- Gottfried Schatz Research Center - Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
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21
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Nothstein M, Luik A, Jadidi A, Sánchez J, Unger LA, Wülfers EM, Dössel O, Seemann G, Schmitt C, Loewe A. CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution. Front Physiol 2021; 12:673047. [PMID: 34108887 PMCID: PMC8181407 DOI: 10.3389/fphys.2021.673047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/30/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Rate-varying S1S2 stimulation protocols can be used for restitution studies to characterize atrial substrate, ionic remodeling, and atrial fibrillation risk. Clinical restitution studies with numerous patients create large amounts of these data. Thus, an automated pipeline to evaluate clinically acquired S1S2 stimulation protocol data necessitates consistent, robust, reproducible, and precise evaluation of local activation times, electrogram amplitude, and conduction velocity. Here, we present the CVAR-Seg pipeline, developed focusing on three challenges: (i) No previous knowledge of the stimulation parameters is available, thus, arbitrary protocols are supported. (ii) The pipeline remains robust under different noise conditions. (iii) The pipeline supports segmentation of atrial activities in close temporal proximity to the stimulation artifact, which is challenging due to larger amplitude and slope of the stimulus compared to the atrial activity. METHODS AND RESULTS The S1 basic cycle length was estimated by time interval detection. Stimulation time windows were segmented by detecting synchronous peaks in different channels surpassing an amplitude threshold and identifying time intervals between detected stimuli. Elimination of the stimulation artifact by a matched filter allowed detection of local activation times in temporal proximity. A non-linear signal energy operator was used to segment periods of atrial activity. Geodesic and Euclidean inter electrode distances allowed approximation of conduction velocity. The automatic segmentation performance of the CVAR-Seg pipeline was evaluated on 37 synthetic datasets with decreasing signal-to-noise ratios. Noise was modeled by reconstructing the frequency spectrum of clinical noise. The pipeline retained a median local activation time error below a single sample (1 ms) for signal-to-noise ratios as low as 0 dB representing a high clinical noise level. As a proof of concept, the pipeline was tested on a CARTO case of a paroxysmal atrial fibrillation patient and yielded plausible restitution curves for conduction speed and amplitude. CONCLUSION The proposed openly available CVAR-Seg pipeline promises fast, fully automated, robust, and accurate evaluations of atrial signals even with low signal-to-noise ratios. This is achieved by solving the proximity problem of stimulation and atrial activity to enable standardized evaluation without introducing human bias for large data sets.
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Affiliation(s)
- Mark Nothstein
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Amir Jadidi
- Klinik für Kardiologie und Angiologie II, University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jorge Sánchez
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Laura A. Unger
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Eike M. Wülfers
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg-Bad Krozingen, Freiburg, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Gunnar Seemann
- Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Institute for Experimental Cardiovascular Medicine, University Heart Center Freiburg-Bad Krozingen, Freiburg, Germany
| | - Claus Schmitt
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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22
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Bifulco SF, Scott GD, Sarairah S, Birjandian Z, Roney CH, Niederer SA, Mahnkopf C, Kuhnlein P, Mitlacher M, Tirschwell D, Longstreth WT, Akoum N, Boyle PM. Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate. eLife 2021; 10:e64213. [PMID: 33942719 PMCID: PMC8143793 DOI: 10.7554/elife.64213] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 04/16/2021] [Indexed: 12/25/2022] Open
Abstract
Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate's arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7 ± 5.45%) than non-inducible models (11.07 ± 3.61%; p<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (p=0.90), meaning that the intrinsic pro-arrhythmic substrate properties of fibrosis in ESUS and AFib are indistinguishable. This suggests that some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers.
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Affiliation(s)
- Savannah F Bifulco
- Department of Bioengineering, University of WashingtonSeattleUnited States
| | - Griffin D Scott
- Department of Bioengineering, University of WashingtonSeattleUnited States
| | - Sakher Sarairah
- Division of Cardiology, University of WashingtonSeattleUnited States
| | - Zeinab Birjandian
- Division of Cardiology, University of WashingtonSeattleUnited States
- Department of Neurology, University of WashingtonSeattleUnited States
| | - Caroline H Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | | | | | | | - David Tirschwell
- Department of Neurology, University of WashingtonSeattleUnited States
| | - WT Longstreth
- Department of Neurology, University of WashingtonSeattleUnited States
- Department of Epidemiology, University of WashingtonSeattleUnited States
| | - Nazem Akoum
- Division of Cardiology, University of WashingtonSeattleUnited States
| | - Patrick M Boyle
- Department of Bioengineering, University of WashingtonSeattleUnited States
- Center for Cardiovascular Biology, University of WashingtonSeattleUnited States
- Institute for Stem Cell and Regenerative Medicine, University of WashingtonSeattleUnited States
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23
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Salinet J, Molero R, Schlindwein FS, Karel J, Rodrigo M, Rojo-Álvarez JL, Berenfeld O, Climent AM, Zenger B, Vanheusden F, Paredes JGS, MacLeod R, Atienza F, Guillem MS, Cluitmans M, Bonizzi P. Electrocardiographic Imaging for Atrial Fibrillation: A Perspective From Computer Models and Animal Experiments to Clinical Value. Front Physiol 2021; 12:653013. [PMID: 33995122 PMCID: PMC8120164 DOI: 10.3389/fphys.2021.653013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/22/2021] [Indexed: 01/16/2023] Open
Abstract
Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.
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Affiliation(s)
- João Salinet
- Biomedical Engineering, Centre for Engineering, Modelling and Applied Social Sciences (CECS), Federal University of ABC, São Bernardo do Campo, Brazil
| | - Rubén Molero
- ITACA Institute, Universitat Politècnica de València, València, Spain
| | - Fernando S. Schlindwein
- School of Engineering, University of Leicester, United Kingdom and National Institute for Health Research, Leicester Cardiovascular Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Joël Karel
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, Netherlands
| | - Miguel Rodrigo
- Electronic Engineering Department, Universitat de València, València, Spain
| | - José Luis Rojo-Álvarez
- Department of Signal Theory and Communications and Telematic Systems and Computation, University Rey Juan Carlos, Madrid, Spain
| | - Omer Berenfeld
- Center for Arrhythmia Research, University of Michigan, Ann Arbor, MI, United States
| | - Andreu M. Climent
- ITACA Institute, Universitat Politècnica de València, València, Spain
| | - Brian Zenger
- Biomedical Engineering Department, Scientific Computing and Imaging Institute (SCI), and Cardiovascular Research and Training Institute (CVRTI), The University of Utah, Salt Lake City, UT, United States
| | - Frederique Vanheusden
- Department of Engineering, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom
| | - Jimena Gabriela Siles Paredes
- Biomedical Engineering, Centre for Engineering, Modelling and Applied Social Sciences (CECS), Federal University of ABC, São Bernardo do Campo, Brazil
| | - Rob MacLeod
- Biomedical Engineering Department, Scientific Computing and Imaging Institute (SCI), and Cardiovascular Research and Training Institute (CVRTI), The University of Utah, Salt Lake City, UT, United States
| | - Felipe Atienza
- Cardiology Department, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, and Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - María S. Guillem
- ITACA Institute, Universitat Politècnica de València, València, Spain
| | - Matthijs Cluitmans
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, Netherlands
| | - Pietro Bonizzi
- Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, Netherlands
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24
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Williams SE, Roney CH, Connolly A, Sim I, Whitaker J, O’Hare D, Kotadia I, O’Neill L, Corrado C, Bishop M, Niederer SA, Wright M, O’Neill M, Linton NWF. OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research. Front Physiol 2021; 12:646023. [PMID: 33716795 PMCID: PMC7952326 DOI: 10.3389/fphys.2021.646023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 01/29/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Electroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner. METHODS A data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research. RESULTS The average time to parse clinical datasets was 400 ± 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 ± 8.7 Mb, vs clinical: 1.46 ± 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: R 2 = 0.7726, P < 0.0001; Volume: R 2 = 0.5179, P < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage R 2 = 0.8708, P < 0.001; local activation time R 2 = 0.8892, P < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies. CONCLUSIONS We present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development.
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Affiliation(s)
- Steven E. Williams
- King’s College London, London, United Kingdom
- Centre for Cardiovascular Science, The University of Edinburgh, Edinburgh, United Kingdom
| | | | - Adam Connolly
- King’s College London, London, United Kingdom
- Invicro, Ltd., London, United Kingdom
| | - Iain Sim
- King’s College London, London, United Kingdom
| | | | | | | | | | | | | | | | - Matt Wright
- King’s College London, London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Mark O’Neill
- King’s College London, London, United Kingdom
- Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
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25
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PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. M&MS AND EMIDEC CHALLENGES 2021. [DOI: 10.1007/978-3-030-68107-4_8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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26
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Kotadia I, Whitaker J, Roney C, Niederer S, O’Neill M, Bishop M, Wright M. Anisotropic Cardiac Conduction. Arrhythm Electrophysiol Rev 2020; 9:202-210. [PMID: 33437488 PMCID: PMC7788398 DOI: 10.15420/aer.2020.04] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 10/09/2020] [Indexed: 01/06/2023] Open
Abstract
Anisotropy is the property of directional dependence. In cardiac tissue, conduction velocity is anisotropic and its orientation is determined by myocyte direction. Cell shape and size, excitability, myocardial fibrosis, gap junction distribution and function are all considered to contribute to anisotropic conduction. In disease states, anisotropic conduction may be enhanced, and is implicated, in the genesis of pathological arrhythmias. The principal mechanism responsible for enhanced anisotropy in disease remains uncertain. Possible contributors include changes in cellular excitability, changes in gap junction distribution or function and cellular uncoupling through interstitial fibrosis. It has recently been demonstrated that myocyte orientation may be identified using diffusion tensor magnetic resonance imaging in explanted hearts, and multisite pacing protocols have been proposed to estimate myocyte orientation and anisotropic conduction in vivo. These tools have the potential to contribute to the understanding of the role of myocyte disarray and anisotropic conduction in arrhythmic states.
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Affiliation(s)
- Irum Kotadia
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, UK
- Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, UK
- Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Caroline Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, UK
| | - Steven Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, UK
| | - Mark O’Neill
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, UK
- Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Martin Bishop
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, UK
| | - Matthew Wright
- School of Biomedical Engineering and Imaging Sciences, King’s College, London, UK
- Guy’s and St Thomas’ NHS Foundation Trust, London, UK
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27
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Koneshloo A, Du D, Du Y. An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis. Bioengineering (Basel) 2020; 7:E62. [PMID: 32604784 PMCID: PMC7355499 DOI: 10.3390/bioengineering7020062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/22/2020] [Accepted: 06/24/2020] [Indexed: 11/16/2022] Open
Abstract
Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.
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Affiliation(s)
- Amirhossein Koneshloo
- Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Dongping Du
- Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Yuncheng Du
- Department of Chemical & Biomolecular Engineering, Clarkson University, Potsdam, NY 13699, USA
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28
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Coveney S, Corrado C, Roney CH, O’Hare D, Williams SE, O’Neill MD, Niederer SA, Clayton RH, Oakley JE, Wilkinson RD. Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20190345. [PMID: 32448072 PMCID: PMC7287339 DOI: 10.1098/rsta.2019.0345] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/21/2020] [Indexed: 05/21/2023]
Abstract
In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian process manifold interpolation (GPMI) method accounts for the topology of the atrium, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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Affiliation(s)
- Sam Coveney
- Insigneo Institute for in-silico medicine and Department of Computer Science, University of Sheffield, Sheffield, UK
- e-mail:
| | - Cesare Corrado
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Caroline H. Roney
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Daniel O’Hare
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Steven E. Williams
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Mark D. O’Neill
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Steven A. Niederer
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Richard H. Clayton
- Insigneo Institute for in-silico medicine and Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Jeremy E. Oakley
- School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
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29
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Abstract
Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67-3.60 ms, and for RA fields: 2.29-3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7-16.6%; range for RA 11.9-15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties.
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30
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Corrado C, Razeghi O, Roney C, Coveney S, Williams S, Sim I, O'Neill M, Wilkinson R, Oakley J, Clayton RH, Niederer S. Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions. Med Image Anal 2020; 61:101626. [PMID: 32000114 DOI: 10.1016/j.media.2019.101626] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 11/05/2019] [Accepted: 12/05/2019] [Indexed: 12/11/2022]
Abstract
Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times.
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Affiliation(s)
- Cesare Corrado
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom.
| | - Orod Razeghi
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Caroline Roney
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Sam Coveney
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven Williams
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Iain Sim
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Mark O'Neill
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Richard Wilkinson
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Jeremy Oakley
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven Niederer
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
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31
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Abstract
Determining optimal treatment strategies for complex arrhythmogenesis in AF is confounded by the lack of consensus regarding the mechanisms causing AF. Studies report different mechanisms for AF, ranging from hierarchical drivers to anarchical multiple activation wavelets. Differences in the assessment of AF mechanisms are likely due to AF being recorded across diverse models using different investigational tools, spatial scales and clinical populations. The authors review different AF mechanisms, including anatomical and functional re-entry, hierarchical drivers and anarchical multiple wavelets. They then describe different cardiac mapping techniques and analysis tools, including activation mapping, phase mapping and fibrosis identification. They explain and review different data challenges, including differences between recording devices in spatial and temporal resolutions, spatial coverage and recording surface, and report clinical outcomes using different data modalities. They suggest future research directions for investigating the mechanisms underlying human AF.
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Affiliation(s)
- Caroline H Roney
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Andrew L Wit
- Imperial Centre for Cardiac Engineering, Imperial College London, London, UK.,Department of Pharmacology, Columbia University College of Physicians and Surgeons, New York, NY, US
| | - Nicholas S Peters
- Imperial Centre for Cardiac Engineering, Imperial College London, London, UK.,National Heart and Lung Institute, Imperial College London, London, UK
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32
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Coveney S, Corrado C, Roney CH, Wilkinson RD, Oakley JE, Lindgren F, Williams SE, O'Neill MD, Niederer SA, Clayton RH. Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds. IEEE Trans Biomed Eng 2020; 67:99-109. [PMID: 30969911 DOI: 10.1109/tbme.2019.2908486] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Local activation time (LAT) mapping of the atria is important for targeted treatment of atrial arrhythmias, but current methods do not interpolate on the atrial manifold and neglect uncertainties associated with LAT observations. In this paper, we describe novel methods to, first, quantify uncertainties in LAT arising from bipolar electrogram analysis and assignment of electrode recordings to the anatomical mesh, second, interpolate uncertain LAT measurements directly on left atrial manifolds to obtain complete probabilistic activation maps, and finally, interpolate LAT jointly across both the manifold and different S1-S2 pacing protocols. METHODS A modified center of mass approach was used to process bipolar electrograms, yielding a LAT estimate and error distribution from the electrogram morphology. An error distribution for assigning measurements to the anatomical mesh was estimated. Probabilistic LAT maps were produced by interpolating on a left atrial manifold using Gaussian Markov random fields, taking into account observation errors and characterizing LAT predictions by their mean and standard deviation. This approach was extended to interpolate across S1-S2 pacing protocols. RESULTS We evaluated our approach using recordings from three patients undergoing atrial ablation. Cross-validation showed consistent and accurate prediction of LAT observations both at different locations on the left atrium and for different S1-S2 intervals. SIGNIFICANCE Interpolation of scalar and vector fields across anatomical structures from point measurements is a challenging problem in biomedical engineering, compounded by uncertainties in measurements and meshes. New methods and approaches are required, and in this paper, we have demonstrated an effective method for probabilistic interpolation of uncertain LAT.
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Ciaccio EJ, Wan EY, Saluja DS, Acharya UR, Peters NS, Garan H. Addressing challenges of quantitative methodologies and event interpretation in the study of atrial fibrillation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 178:113-122. [PMID: 31416540 PMCID: PMC6748794 DOI: 10.1016/j.cmpb.2019.06.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 05/21/2019] [Accepted: 06/14/2019] [Indexed: 05/06/2023]
Abstract
Atrial fibrillation (AF) is the commonest arrhythmia, yet the mechanisms of its onset and persistence are incompletely known. Although techniques for quantitative assessment have been investigated, there have been few attempts to integrate this information to advance disease treatment protocols. In this review, key quantitative methods for AF analysis are described, and suggestions are provided for the coordination of the available information, and to develop foci and directions for future research efforts. Quantitative biologists may have an interest in this topic in order to develop machine learning and tools for arrhythmia characterization, but they may perhaps have a minimal background in the clinical methodology and in the types of observed events and mechanistic hypotheses that have thus far been developed. We attempt to address these issues via exploration of the published literature. Although no new data is presented in this review, examples are shown of current lines of investigation, and in particular, how electrogram analysis and whole-chamber quantitative modeling of the left atrium may be useful to characterize fibrillatory patterns of activity, so as to propose avenues for more efficacious acquisition and interpretation of AF data.
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Affiliation(s)
- Edward J Ciaccio
- Department of Medicine - Division of Cardiology, Columbia University Medical Center, New York, NY, USA; ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK.
| | - Elaine Y Wan
- Department of Medicine - Division of Cardiology, Columbia University Medical Center, New York, NY, USA
| | - Deepak S Saluja
- Department of Medicine - Division of Cardiology, Columbia University Medical Center, New York, NY, USA
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
| | - Nicholas S Peters
- ElectroCardioMaths Programme, Imperial Centre for Cardiac Engineering, Imperial College London, London, UK
| | - Hasan Garan
- Department of Medicine - Division of Cardiology, Columbia University Medical Center, New York, NY, USA
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Aronis KN, Ali RL, Liang JA, Zhou S, Trayanova NA. Understanding AF Mechanisms Through Computational Modelling and Simulations. Arrhythm Electrophysiol Rev 2019; 8:210-219. [PMID: 31463059 PMCID: PMC6702471 DOI: 10.15420/aer.2019.28.2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 06/17/2019] [Indexed: 12/21/2022] Open
Abstract
AF is a progressive disease of the atria, involving complex mechanisms related to its initiation, maintenance and progression. Computational modelling provides a framework for integration of experimental and clinical findings, and has emerged as an essential part of mechanistic research in AF. The authors summarise recent advancements in development of multi-scale AF models and focus on the mechanistic links between alternations in atrial structure and electrophysiology with AF. Key AF mechanisms that have been explored using atrial modelling are pulmonary vein ectopy; atrial fibrosis and fibrosis distribution; atrial wall thickness heterogeneity; atrial adipose tissue infiltration; development of repolarisation alternans; cardiac ion channel mutations; and atrial stretch with mechano-electrical feedback. They review modelling approaches that capture variability at the cohort level and provide cohort-specific mechanistic insights. The authors conclude with a summary of future perspectives, as envisioned for the contributions of atrial modelling in the mechanistic understanding of AF.
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Affiliation(s)
- Konstantinos N Aronis
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
- Division of Cardiology, Johns Hopkins HospitalBaltimore, MD, US
| | - Rheeda L Ali
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
| | - Jialiu A Liang
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
| | - Shijie Zhou
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
| | - Natalia A Trayanova
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins UniversityBaltimore, MD, US
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