Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method

Abstract

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.

Assessment of carotid stiffness by measuring carotid pulse wave velocity using a single‐slice oblique‐sagittal phase‐contrast MRI

“…We found that the TTF method resulted in significantly larger cPWV values compared to those obtained with the MUSA technique (Figure 4). Similar discrepancies between different transit‐time quantification algorithms has been reported in previous studies 29,37 . Compared to MUSA‐derived cPWV values, cPWV values obtained based on the TTF algorithm were closer to previously reported values in healthy volunteers obtained using ultrasound 14,34,49 and PC‐MRI 24 techniques.…”

“…For each axial plane, the transit-time between the averaged velocity waveform and the velocity waveform corresponding to each in-plane location was calculated using the MUSA method ( Figure 1D transit-time quantification algorithms has been reported in previous studies. 29,37 Compared to MUSA-derived cPWV values, cPWV values obtained based on the TTF algorithm were closer to previously reported values in healthy volunteers obtained using ultrasound 14,34,49 and PC-MRI 24 techniques. This was expected as the majority of previous cPWV quantifications were performed using the TTF algorithm.…”

“…TTF method has been used extensively to quantify transit time of velocity waveforms with high temporal resolution obtained from ultrasound 19,26,50 . However, recent studies have reported that other transit‐time quantification algorithms could be more reliable when dealing with relatively lower temporal resolutions in PC‐MRI–based PWV measurements 29,37,51 . Also, it has been suggested that choosing the best algorithms might depend on the velocity waveform characteristics that are known to be different in various regions of vasculatures 29 .…”

“…19,26,50 However, recent studies have reported that other transit-time quantification algorithms could be more reliable when dealing with relatively lower temporal resolutions in PC-MRI-based PWV measurements. 29,37,51 Also, it has been suggested that choosing the best algorithms might depend on the velocity waveform characteristics that are known to be different in various regions of vasculatures. 29 Further studies are needed to evaluate the impact of transit-time quantification algorithms on the accuracy of PC-MRI-derived PWV measurements in cerebral arteries.…”

Cambios en la función endotelial vascular de pacientes con artritis reumatoidea en tratamiento

“…Sin embargo, este exceso de mortalidad, no se explica por los factores de riesgo cardiovascular tradicionales (8)(9)(10)(11). La disfunción endotelial, la rigidez arterial y las anomalías vasculares estructurales son probados marcadores surrogados de aterosclerosis prematura, potencialmente reversible y de eventos cardiovasculares (12)(13)(14)(15)(16)(17). Estos marcadores podrían evaluarse midiendo la velocidad de la onda de pulso carótido-femoral (VOP) y el grosor íntima-media de la arteria carótida común (EIM-ACC), respectivamente.…”