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. Conclusion: 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.