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颅 4D 流 MRI 的自动血流动力学评估。

Automated hemodynamic assessment for cranial 4D flow MRI.

机构信息

Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, 1111 Highland Avenue #1005, Madison, WI 53705, USA.

Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, 1111 Highland Avenue #1005, Madison, WI 53705, USA.

出版信息

Magn Reson Imaging. 2023 Apr;97:46-55. doi: 10.1016/j.mri.2022.12.016. Epub 2022 Dec 26.

Abstract

Cranial 4D flow MRI post-processing typically involves manual user interaction which is time-consuming and associated with poor repeatability. The primary goal of this study is to develop a robust quantitative velocity tool (QVT) that utilizes threshold-based segmentation techniques to improve segmentation quality over prior approaches based on centerline processing schemes (CPS) that utilize k-means clustering segmentation. This tool also includes an interactive 3D display designed for simplified vessel selection and automated hemodynamic visualization and quantification. The performances of QVT and CPS were compared in vitro in a flow phantom and in vivo in 10 healthy participants. Vessel segmentations were compared with ground-truth computed tomography in vitro (29 locations) and manual segmentation in vivo (13 locations) using linear regression. Additionally, QVT and CPS MRI flow rates were compared to perivascular ultrasound flow in vitro using linear regression. To assess internal consistency of flow measures in vivo, conservation of flow was assessed at vessel junctions using linear regression and consistency of flow along vessel segments was analyzed by fitting a Gaussian distribution to a histogram of normalized flow values. Post-processing times were compared between the QVT and CPS using paired t-tests. Vessel areas segmented in vitro (CPS: slope = 0.71, r = 0.95 and QVT: slope = 1.03, r = 0.95) and in vivo (CPS: slope = 0.61, r = 0.96 and QVT: slope = 0.93, r = 0.96) were strongly correlated with ground-truth area measurements. However, CPS (using k-means segmentation) consistently underestimated vessel areas. Strong correlations were observed between QVT and ultrasound flow (slope = 0.98, r = 0.96) as well as flow at junctions (slope = 1.05, r = 0.98). Mean and standard deviation of flow along vessel segments was 9.33e-16 ± 3.05%. Additionally, the QVT demonstrated excellent interobserver agreement and significantly reduced post-processing by nearly 10 min (p < 0.001). By completely automating post-processing and providing an easy-to-use 3D visualization interface for interactive vessel selection and hemodynamic quantification, the QVT offers an efficient, robust, and repeatable means to analyze cranial 4D flow MRI. This software is freely available at: https://github.com/uwmri/QVT.

摘要

颅 4D 血流 MRI 后处理通常需要手动用户交互,这既耗时又重复性差。本研究的主要目标是开发一种强大的定量速度工具 (QVT),该工具利用基于阈值的分割技术,提高基于中心线处理方案 (CPS) 的分割质量,该方案利用 K 均值聚类分割。该工具还包括一个交互式 3D 显示,旨在简化血管选择和自动血流可视化和量化。在体外血流体模中和体内 10 名健康参与者中,比较了 QVT 和 CPS 的性能。使用线性回归比较了体外血管分割与 CT 金标准(29 个位置)和体内手动分割(13 个位置)。此外,使用线性回归比较了 QVT 和 CPS MRI 流量与体外血管周围超声流量。为了评估体内血流测量的内部一致性,使用线性回归评估了血管连接处的血流守恒性,并通过对归一化流量值的直方图拟合高斯分布来分析血管段内的流量一致性。使用配对 t 检验比较了 QVT 和 CPS 之间的后处理时间。体外分割的血管面积(CPS:斜率= 0.71,r = 0.95,QVT:斜率= 1.03,r = 0.95)和体内分割的血管面积(CPS:斜率= 0.61,r = 0.96,QVT:斜率= 0.93,r = 0.96)与金标准面积测量值具有很强的相关性。然而,CPS(使用 K 均值分割)始终低估了血管面积。QVT 与超声流量(斜率= 0.98,r = 0.96)以及连接处的流量(斜率= 1.05,r = 0.98)之间也存在很强的相关性。血管段内的平均流量和标准偏差为 9.33e-16 ± 3.05%。此外,QVT 表现出良好的观察者间一致性,并将后处理时间减少了近 10 分钟(p < 0.001)。通过完全自动化后处理并提供易于使用的 3D 可视化界面,用于交互式血管选择和血流动力学量化,QVT 提供了一种高效、强大且可重复的分析颅 4D 血流 MRI 的方法。该软件可在以下网址免费获得:https://github.com/uwmri/QVT。

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