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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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