A three-dimensional left atrial motion estimation from retrospective gated computed tomography: application in heart failure patients with atrial fibrillation.

atrial fibrillation heart failure left atrial strain retrospective gated computed tomography strain imaging

Journal

Frontiers in cardiovascular medicine
ISSN: 2297-055X
Titre abrégé: Front Cardiovasc Med
Pays: Switzerland
ID NLM: 101653388

Informations de publication

Date de publication:
2024
Historique:
received: 21 12 2023
accepted: 08 03 2024
medline: 10 4 2024
pubmed: 10 4 2024
entrez: 10 4 2024
Statut: epublish

Résumé

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. 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. A cohort of 30 HFrEF patients with ( 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%, 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.

Sections du résumé

Background UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
A cohort of 30 HFrEF patients with (
Results UNASSIGNED
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%,
Conclusion UNASSIGNED
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.

Identifiants

pubmed: 38596691
doi: 10.3389/fcvm.2024.1359715
pmc: PMC11002108
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1359715

Informations de copyright

© 2024 Sillett, Razeghi, Lee, Solis Lemus, Roney, Mannina, de Vere, Ananthan, Ennis, Haberland, Xu, Young, Rinaldi, Rajani and Niederer.

Déclaration de conflit d'intérêts

CS reports financial support from Siemens. UH is an employee of Siemens. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Charles Sillett (C)

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 (O)

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 (AWC)

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 (JA)

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 (C)

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 (C)

Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.

Felicity de Vere (F)

Department of Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.

Kiruthika Ananthan (K)

Department of Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, United Kingdom.

Daniel B Ennis (DB)

Department of Radiology, Stanford University, Stanford, CA, United States.

Ulrike Haberland (U)

Siemens Healthcare GmbH, Forchheim, Germany.

Hao Xu (H)

School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

Alistair Young (A)

School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

Christopher A Rinaldi (CA)

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 (R)

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 (SA)

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.

Classifications MeSH