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

Author:

Merino-Caviedes Susana,Gutierrez Lilian K.,Alfonso-Almazán José Manuel,Sanz-Estébanez Santiago,Cordero-Grande Lucilio,Quintanilla Jorge G.,Sánchez-González Javier,Marina-Breysse Manuel,Galán-Arriola Carlos,Enríquez-Vázquez Daniel,Torres Carlos,Pizarro Gonzalo,Ibáñez Borja,Peinado Rafael,Merino Jose Luis,Pérez-Villacastín Julián,Jalife José,López-Yunta Mariña,Vázquez Mariano,Aguado-Sierra Jazmín,González-Ferrer Juan José,Pérez-Castellano Nicasio,Martín-Fernández Marcos,Alberola-López Carlos,Filgueiras-Rama David

Abstract

AbstractDelayed 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.

Funder

Ministerio de Ciencia e Innovación

ProCNIC Foundation

European Regional Development Fund

Horizon 2020 Framework Programme

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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