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Sillett C, Razeghi O, Lee AWC, Solis Lemus JA, Roney C, Mannina C, de Vere F, Ananthan K, Ennis DB, Haberland U, Xu H, Young A, Rinaldi CA, Rajani R, Niederer SA. A three-dimensional left atrial motion estimation from retrospective gated computed tomography: application in heart failure patients with atrial fibrillation. Front Cardiovasc Med 2024; 11:1359715. [PMID: 38596691 PMCID: PMC11002108 DOI: 10.3389/fcvm.2024.1359715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
Background A reduced left atrial (LA) strain correlates with the presence of atrial fibrillation (AF). Conventional atrial strain analysis uses two-dimensional (2D) imaging, which is, however, limited by atrial foreshortening and an underestimation of through-plane motion. Retrospective gated computed tomography (RGCT) produces high-fidelity three-dimensional (3D) images of the cardiac anatomy throughout the cardiac cycle that can be used for estimating 3D mechanics. Its feasibility for LA strain measurement, however, is understudied. Aim The aim of this study is to develop and apply a novel workflow to estimate 3D LA motion and calculate the strain from RGCT imaging. The utility of global and regional strains to separate heart failure in patients with reduced ejection fraction (HFrEF) with and without AF is investigated. Methods A cohort of 30 HFrEF patients with (n = 9) and without (n = 21) AF underwent RGCT prior to cardiac resynchronisation therapy. The temporal sparse free form deformation image registration method was optimised for LA feature tracking in RGCT images and used to estimate 3D LA endocardial motion. The area and fibre reservoir strains were calculated over the LA body. Universal atrial coordinates and a human atrial fibre atlas enabled the regional strain calculation and the fibre strain calculation along the local myofibre orientation, respectively. Results It was found that global reservoir strains were significantly reduced in the HFrEF + AF group patients compared with the HFrEF-only group patients (area strain: 11.2 ± 4.8% vs. 25.3 ± 12.6%, P = 0.001; fibre strain: 4.5 ± 2.0% vs. 15.2 ± 8.8%, P = 0.001), with HFrEF + AF patients having a greater regional reservoir strain dyssynchrony. All regional reservoir strains were reduced in the HFrEF + AF patient group, in whom the inferior wall strains exhibited the most significant differences. The global reservoir fibre strain and LA volume + posterior wall reservoir fibre strain exceeded LA volume alone and 2D global longitudinal strain (GLS) for AF classification (area-under-the-curve: global reservoir fibre strain: 0.94 ± 0.02, LA volume + posterior wall reservoir fibre strain: 0.95 ± 0.02, LA volume: 0.89 ± 0.03, 2D GLS: 0.90 ± 0.03). Conclusion RGCT enables 3D LA motion estimation and strain calculation that outperforms 2D strain metrics and LA enlargement for AF classification. Differences in regional LA strain could reflect regional myocardial properties such as atrial fibrosis burden.
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Affiliation(s)
- Charles Sillett
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Angela W. C. Lee
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Jose Alonso Solis Lemus
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Caroline Roney
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- School of Engineering and Materials Science, Queen Mary University of London, London, United Kingdom
| | - Carlo Mannina
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Felicity de Vere
- Department of Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Kiruthika Ananthan
- Department of Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Daniel B. Ennis
- Department of Radiology, Stanford University, Stanford, CA, United States
| | | | - Hao Xu
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alistair Young
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Christopher A. Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Ronak Rajani
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Cardiology, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Steven A. Niederer
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
- Turing Research and Innovation Cluster: Digital Twins, The Alan Turing Institute, London, United Kingdom
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Roney CH, Sillett C, Whitaker J, Lemus JAS, Sim I, Kotadia I, O'Neill M, Williams SE, Niederer SA. Applications of multimodality imaging for left atrial catheter ablation. Eur Heart J Cardiovasc Imaging 2021; 23:31-41. [PMID: 34747450 PMCID: PMC8685603 DOI: 10.1093/ehjci/jeab205] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
Atrial arrhythmias, including atrial fibrillation and atrial flutter, may be treated through catheter ablation. The process of atrial arrhythmia catheter ablation, which includes patient selection, pre-procedural planning, intra-procedural guidance, and post-procedural assessment, is typically characterized by the use of several imaging modalities to sequentially inform key clinical decisions. Increasingly, advanced imaging modalities are processed via specialized image analysis techniques and combined with intra-procedural electrical measurements to inform treatment approaches. Here, we review the use of multimodality imaging for left atrial ablation procedures. The article first outlines how imaging modalities are routinely used in the peri-ablation period. We then describe how advanced imaging techniques may inform patient selection for ablation and ablation targets themselves. Ongoing research directions for improving catheter ablation outcomes by using imaging combined with advanced analyses for personalization of ablation targets are discussed, together with approaches for their integration in the standard clinical environment. Finally, we describe future research areas with the potential to improve catheter ablation outcomes.
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Affiliation(s)
- Caroline H Roney
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Charles Sillett
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - John Whitaker
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | | | - Iain Sim
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
| | - Irum Kotadia
- 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
| | - Steven E Williams
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
- Centre for Cardiovascular Science, The University of Edinburgh, Scotland, UK
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College, London, UK
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