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Time-efficient three-dimensional transmural scar assessment provides relevant substrate characterization for ventricular tachycardia features and long-term recurrences in ischemic cardiomyopathy.

Authors :
Merino-Caviedes S
Gutierrez LK
Alfonso-Almazán JM
Sanz-Estébanez S
Cordero-Grande L
Quintanilla JG
Sánchez-González J
Marina-Breysse M
Galán-Arriola C
Enríquez-Vázquez D
Torres C
Pizarro G
Ibáñez B
Peinado R
Merino JL
Pérez-Villacastín J
Jalife J
López-Yunta M
Vázquez M
Aguado-Sierra J
González-Ferrer JJ
Pérez-Castellano N
Martín-Fernández M
Alberola-López C
Filgueiras-Rama D
Source :
Scientific reports [Sci Rep] 2021 Sep 28; Vol. 11 (1), pp. 18722. Date of Electronic Publication: 2021 Sep 28.
Publication Year :
2021

Abstract

Delayed gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging requires novel and time-efficient approaches to characterize the myocardial substrate associated with ventricular arrhythmia in patients with ischemic cardiomyopathy. Using a translational approach in pigs and patients with established myocardial infarction, we tested and validated a novel 3D methodology to assess ventricular scar using custom transmural criteria and a semiautomatic approach to obtain transmural scar maps in ventricular models reconstructed from both 3D-acquired and 3D-upsampled-2D-acquired LGE-CMR images. The results showed that 3D-upsampled models from 2D LGE-CMR images provided a time-efficient alternative to 3D-acquired sequences to assess the myocardial substrate associated with ischemic cardiomyopathy. Scar assessment from 2D-LGE-CMR sequences using 3D-upsampled models was superior to conventional 2D assessment to identify scar sizes associated with the cycle length of spontaneous ventricular tachycardia episodes and long-term ventricular tachycardia recurrences after catheter ablation. This novel methodology may represent an efficient approach in clinical practice after manual or automatic segmentation of myocardial borders in a small number of conventional 2D LGE-CMR slices and automatic scar detection.<br /> (© 2021. The Author(s).)

Details

Language :
English
ISSN :
2045-2322
Volume :
11
Issue :
1
Database :
MEDLINE
Journal :
Scientific reports
Publication Type :
Academic Journal
Accession number :
34580343
Full Text :
https://doi.org/10.1038/s41598-021-97399-w