Visual standardized quantification of late gadolinium enhancement, a contour-less method for late gadolinium enhancement quantification

Late gadolinium enhancement (LGE) quantification by cardiovascular magnetic resonance is central to risk stratification in hypertrophic cardiomyopathy (HCM). Conventional techniques require contour tracing and region of interest (ROI) placement, which may reduce reproducibility and increase analysis time. We developed a novel visual approach, the visual standardized quantification (VISTAQ) of LGE, that does not require myocardial contouring, arbitrary ROI positioning, or dedicated post-processing software.

LGE images from 400 patients (100 prior myocardial infarction, 250 HCM, and 50 other non-ischaemic heart diseases) were analysed. Reproducibility was assessed using the intra-class correlation coefficients (ICC) and Bland–Altman analysis. VISTAQ was compared with conventional methods [mean+2SD, +5SD, +6SD, full-width-at-half-maximum (FWHM), visual thresholding]. Prognostic performance was evaluated in 250 HCM patients. VISTAQ demonstrated excellent intra- and inter-observer reproducibility (ICC up to 0.98 and 0.97, respectively), consistent across disease subtypes. VISTAQ showed similar ICC to FWHM but significantly lower net and absolute inter-observer differences (median absolute difference: 1.3%). Mean+2SD markedly overestimated LGE, whereas mean+6SD slightly underestimated LGE compared with VISTAQ, mean+5SD, FWHM, and visual thresholding. Analysis time was shorter with VISTAQ (median 105 vs. 375 s, P < 0.0001). During follow-up, 21 hard cardiac events occurred in HCM population. An LGE threshold >10% predicted events with higher accuracy using VISTAQ [area under the curve (AUC): 0.90; sensitivity: 85%; specificity: 94%) compared with mean+6SD (AUC: 0.75; sensitivity: 57%; specificity: 93%).

VISTAQ provides highly reproducible, time-efficient LGE quantification without dedicated software and demonstrates non-inferior prognostic discrimination in HCM compared with conventional threshold-based techniques.