Background: Electroanatomical voltage mapping (EAVM) has been compared with late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR), which cannot delineate diffuse fibrosis. T1-mapping CMR overcomes the limitations of LGE-CMR, but it has not been directly compared against EAVM. Objectives: This study aims to assess the relationship between left ventricular (LV) endocardial voltage obtained by EAVM and extracellular volume (ECV) obtained by T1 mapping. Methods: The study investigated patients who underwent endocardial EAVM for ventricular arrhythmias (CARTO 3, Biosense Webster) together with preprocedural contrast-enhanced T1 mapping (Ingenia 3T, Philips Healthcare). After image integration, EAVM datapoints were projected onto LGE-CMR and ECV-encoded images. Average values of unipolar voltage (UV), bipolar voltage (BV), LGE transmurality, and ECV were merged from corresponding cardiac segments (6 per slice) and pooled for analysis. Results: The analysis included data from 628 segments from 18 patients (57 ± 13 years of age, 17% females, LV ejection fraction 48% ± 14%, nonischemic/ischemic cardiomyopathy/controls: 8/6/4 patients). Based on the 95th and 5th percentile values obtained from the controls, ECV >33%, BV <2.9 mV, and UV <6.7 mV were considered abnormal. There was a significant inverse association between voltage and ECV, but only in segments with abnormal ECV. Increased ECV could predict abnormal BV and UV with acceptable accuracy (area under the curve of 0.78 [95% CI: 0.74-0.83] and 0.84 [95% CI: 0.79-0.88]). Conclusions: This study found a significant inverse relationship between LV endocardial voltage and ECV. Real-time integration of T1 mapping may guide catheter mapping and may allow identification of areas of diffuse fibrosis potentially related to ventricular arrhythmias.

Background: Aortic pulse wave velocity (PWV) is an indicator of aortic stiffness and is used as a predictor of adverse cardiovascular events. PWV can be non-invasively assessed using magnetic resonance imaging (MRI). PWV computation requires two components, the length of the aortic arch and the time taken for the systolic pressure wave to travel through the aortic arch. The aortic length is calculated using a multi-slice 3D scan and the transit time is computed using a 2D velocity encoded MRI (VE) scan. In this study we present and evaluate an automatic method to quantify the aortic pulse wave velocity using a large population-based cohort. Methods: For this study 212 subjects were retrospectively selected from a large multi-center heart-brain connection cohort. For each subject a multi-slice 3D scan of the aorta was acquired in an oblique-sagittal plane and a 2D VE scan acquired in a transverse plane cutting through the proximal ascending and descending aorta. PWV was calculated in three stages: (i) a multi-atlas-based segmentation method was developed to segment the aortic arch from the multi-slice 3D scan and subsequently estimate the length of the proximal aorta, (ii) an algorithm that delineates the proximal ascending and descending aorta from the time-resolved 2D VE scan and subsequently obtains the velocity-time flow curves was also developed, and (iii) automatic methods that can compute the transit time from the velocity-time flow curves were implemented and investigated. Finally the PWV was obtained by combining the aortic length and the transit time. Results: Quantitative evaluation with respect to the length of the aortic arch as well as the computed PWV were performend by comparing the results of the novel automatic method to those obtained manually. The mean absolute difference in aortic length obtained automatically as compared to those obtained manually was 3.3 ± 2.8 mm (p < 0.05), the manual inter-observer variability on a subset of 45 scans was 3.4 ± 3.4 mm (p = 0.49). Bland-Altman analysis between the automataic method and the manual methods showed a bias of 0.0 (-5.0,5.0) m/s for the foot-to-foot approach, -0.1 (-1.2, 1.1) and -0.2 (-2.6, 2.1) m/s for the half-max and the cross-correlation methods, respectively. Conclusion: We proposed and evaluated a fully automatic method to calculate the PWV on a large set of multi-center MRI scans. It was observed that the overall results obtained had very good agreement with manual analysis. Our proposed automatic method would be very beneficial for large population based studies, where manual analysis requires a lot of manpower.