Optimizing ventricular scar characterization in late-gadolinium enhancement cardiac MRI: Impact of thresholding techniques in magnitude and phase-sensitive reconstructed images.

Background: Late gadolinium enhancement (LGE) images, reconstructed using magnitude (MAG) or phase-sensitive inversion recovery (PSIR) sequences, differ in signal intensities because of their handling of longitudinal magnetization. These differences influence LGE quantification, which typically uses full-width at half maximum (FWHM) or standard deviation (n-SD) thresholding when predicting cardiac events.

Objective: This study assessed the impact of FWHM and n-SD on MAG- and PSIR-derived scar characteristics.

Methods: Patients with ischemic cardiomyopathy undergoing LGE imaging were retrospectively studied. Two reconstruction techniques (MAG vs PSIR) and 2 thresholding methods (FWHM vs n-SD) were evaluated. LGE images were postprocessed with commercially available software, using scar thresholds of 40%-60% of the maximum signal intensity for FWHM and 2-5 SDs above the mean for n-SD. Scar quantification was compared between patients with primary and secondary prevention implantable cardioverter-defibrillator.

Results: Of the 80 patients, 32 (40%) had an implantable cardioverter-defibrillator for primary prevention. PSIR imaging showed significantly larger scar metrics than did MAG using FWHM and n-SD thresholding, including larger border zone (16.43 ± 8.15 g vs 21.42 ± 10.72 g; P<.001) and conduction corridor (CC) characteristics. MAG-based analysis revealed significant differences in scar and CC metrics. For PSIR, scar metrics were consistent across FWHM and n-SD. MAG-based analysis showed larger border zone and CC length in patients with primary prevention, with similar trends for PSIR.

Conclusions: This study demonstrates significant differences in myocardial scar metrics based on reconstruction and thresholding techniques. PSIR consistently provided robust scar characterization across methods, emphasizing its clinical potential to standardize LGE-cardiac magnetic resonance workflows and improve ventricular arrhythmia risk stratification.