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.

中文翻译：

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基于人群的主动脉脉搏波速度的量化：一种全自动方法。


背景技术主动脉脉搏波速度(PWV)是主动脉僵硬度的指标并且被用作不良心血管事件的预测因子。 PWV 可以使用磁共振成像 (MRI) 进行无创评估。 PWV 计算需要两个部分，即主动脉弓的长度和收缩压波穿过主动脉弓所需的时间。主动脉长度使用多切片 3D 扫描计算，传输时间使用 2D 速度编码 MRI (VE) 扫描计算。在这项研究中，我们提出并评估了一种使用大型人群来量化主动脉脉搏波速度的自动方法。方法 本研究从大型多中心心脑连接队列中回顾性选择了 212 名受试者。对于每个受试者，在斜矢状平面中获取主动脉的多切片 3D 扫描，并在穿过近端升主动脉和降主动脉的横向平面中获取 2D VE 扫描。 PWV 分三个阶段计算：(i) 开发了一种基于多图谱的分割方法，从多切片 3D 扫描中分割主动脉弓，然后估计近端主动脉的长度，(ii) 一种描绘主动脉弓的算法还开发了时间分辨 2D VE 扫描的近端升主动脉和降主动脉，并随后获得速度-时间流量曲线，并且 (iii) 实施和研究了可以根据速度-时间流量曲线计算传输时间的自动方法。最后结合主动脉长度和传输时间得到PWV。结果 通过将新颖的自动方法的结果与手动获得的结果进行比较，对主动脉弓的长度以及计算的 PWV 进行了定量评估。 与手动获得的主动脉长度相比，自动获得的主动脉长度的平均绝对差异为 3.3 ± 2.8 mm (p < 0.05)，45 次扫描子集的手动观察者间变异为 3.4 ± 3.4 mm (p = 0.49)。自动方法和手动方法之间的 Bland-Altman 分析显示，对于脚对脚方法，偏差为 0.0 (-5.0,5.0) m/s，-0.1 (-1.2, 1.1) 和 -0.2 (-2.6, 2.1) m/s 分别用于半最大法和互相关法。结论 我们提出并评估了一种在大量多中心 MRI 扫描上计算 PWV 的全自动方法。据观察，获得的总体结果与手动分析非常吻合。我们提出的自动方法对于基于大量人口的研究非常有益，其中手动分析需要大量人力。