Background-Scar signal quantification using late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) identifies patients at higher risk of future events, both in ischemic cardiomyopathy (ICM) and nonischemic dilated cardiomyopathy (DCM). However, the ability of scar signal burden to predict events in such patient groups at the time of referral for implantable cardioverter-defibrillator (ICD) has not been well explored. This study evaluates the predictive use of multiple scar quantification measures in ICM and DCM patients being referred for ICD. Methods and Results-One hundred twenty-four consecutive patients referred for ICD therapy (59 with ICM and 65 with DCM) underwent a standardized LGE-CMR protocol with blinded, multithreshold scar signal quantification and, for those with ICM, peri-infarct signal quantification. Patients were followed prospectively for the primary combined outcome of appropriate ICD therapy, survived cardiac arrest, or sudden cardiac death. At a mean follow-up of 632±262 days, 18 patients (15%) had suffered the primary outcome. Total scar was significantly higher among those suffering a primary outcome, a relationship maintained within each cardiomyopathy cohort (P<0.01 for all comparisons). Total scar was the strongest independent predictor of the primary outcome and demonstrated a negative predictive value of 86%. In the ICM subcohort, peri-infarct signal showed only a nonsignificant trend toward elevation among those having a primary end point. Conclusions-Myocardial

BackgroundThe presence and extent of late gadolinium enhancement (LGE) has been associated with adverse events in patients with hypertrophic cardiomyopathy (HCM). Signal intensity (SI) threshold techniques are routinely employed for quantification; Full-Width at Half-Maximum (FWHM) techniques are suggested to provide greater reproducibility than Signal Threshold versus Reference Mean (STRM) techniques, however the accuracy of these approaches versus the manual assignment of optimal SI thresholds has not been studied. In this study, we compared all known semi-automated LGE quantification techniques for accuracy and reproducibility among patients with HCM.MethodsSeventy-six HCM patients (51 male, age 54 ± 13 years) were studied. Total LGE volume was quantified using 7 semi-automated techniques and compared to expert manual adjustment of the SI threshold to achieve optimal segmentation. Techniques tested included STRM based thresholds of >2, 3, 4, 5 and 6 SD above mean SI of reference myocardium, the FWHM technique, and the Otsu-auto-threshold (OAT) technique. The SI threshold chosen by each technique was recorded for all slices. Bland-Altman analysis and intra-class correlation coefficients (ICC) were reported for each semi-automated technique versus expert, manually adjusted LGE segmentation. Intra- and inter-observer reproducibility assessments were also performed.ResultsFifty-two of 76 (68%) patients showed LGE on a total of 202 slices. For accuracy, the STRM >3SD technique showed the greatest agreement with manual segmentation (ICC = 0.97, mean difference and 95% limits of agreement = 1.6 ± 10.7 g) while STRM >6SD, >5SD, 4SD and FWHM techniques systematically underestimated total LGE volume. Slice based analysis of selected SI thresholds similarly showed the STRM >3SD threshold to most closely approximate manually adjusted SI thresholds (ICC = 0.88). For reproducibility, the intra- and inter-observer reproducibility of the >3SD threshold demonstrated an acceptable mean difference and 95% limits of agreement of −0.5 ± 6.8 g and −0.9 ± 5.6 g, respectively.ConclusionsFWHM segmentation provides superior reproducibility, however systematically underestimates total LGE volume compared to manual segmentation in patients with HCM. The STRM >3SD technique provides the greatest accuracy while retaining acceptable reproducibility and may therefore be a preferred approach for LGE quantification in this population.

We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graphical user interface to initialize a fast max-flow-based segmentation algorithm and segment scar accurately with progressive interaction. We propose a partially-ordered Potts (POP) model to multi-region segmentation to properly encode the known spatial consistency of cardiac regions. Its generalization introduces a custom label/region order constraint to Potts model to multi-region segmentation. The combinatorial optimization problem associated with the proposed POP model is solved by means of convex relaxation, for which a novel multi-level continuous max-flow formulation, i.e., the hierarchical continuous max-flow (HMF) model, is proposed and studied. We demonstrate that the proposed HMF model is dual or equivalent to the convex relaxed POP model and introduces a new and efficient hierarchical continuous max-flow based algorithm by modern convex optimization theory. In practice, the introduced hierarchical continuous max-flow based algorithm can be implemented on the parallel GPU to achieve significant acceleration in numerics. Experiments are performed in 50 whole heart 3-D LE datasets, 35 with left-ventricular and 15 with right-ventricular scar. The experimental results are compared to full-width-at-half-maximum and Signal-threshold to reference-mean methods using manual expert myocardial segmentations and operator variabilities and the effect of user interaction are assessed. The results indicate a substantial reduction in image processing time with robust accuracy for detection of myocardial scar. This is achieved without the need for additional region constraints and using a single optimization procedure, substantially reducing the potential for error.