Loecher Michael, Schrauben Eric, Johnson Kevin M, Wieben Oliver
Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
J Magn Reson Imaging. 2016 Apr;43(4):833-42. doi: 10.1002/jmri.25045. Epub 2015 Sep 28.
To introduce and demonstrate a method for unwrapping 4D flow data by utilizing continuity constraints in all four available dimensions.
A Laplacian-based algorithm for unwrapping phase data was expanded to unwrap along the temporal dimension in addition to all three spatial dimensions. The method was tested on simulated blood flow under varying vessel diameters and velocity encoding (Venc ) values. The algorithm was also tested in the aorta of five volunteers, with wrapped data acquired with Venc = 80 cm/s and 40 cm/s. Unwrapping performance was measured visually and in comparison to a high Venc reference free of phase wrapping. Ten patients with aortic coarctations with clinical Venc values and lower-Venc reconstructions were corrected and scored by blinded reviewers on a 0-3 scale.
Simulated data were completely unwrapped for most clinically relevant levels of velocity aliasing using the proposed method. In vivo data in the aorta were completely unwrapped for cases of moderate wrapping (Venc = 80 cm/s, peak velocities = ∼160 cm/s), while residual aliasing remained for the more considerably aliased datasets (Venc = 40 cm/s). Improvements were seen in scoring (mean score improved by 1.1 and 2.2 for clinical and low-Venc datasets, respectively) by the blinded reviewers in the patient cohort for both standard and low-Venc reconstructions.
A computationally fast, fully automated, easy to use, and parameter-free single-step method for unwrapping 4D flow data is shown to be effective for use in most common clinical occurrences of velocity aliasing.
介绍并演示一种通过利用所有四个可用维度中的连续性约束来展开4D流数据的方法。
一种基于拉普拉斯算子的相位数据展开算法被扩展,除了在所有三个空间维度上展开外,还能在时间维度上展开。该方法在不同血管直径和速度编码(Venc)值下的模拟血流上进行了测试。该算法还在五名志愿者的主动脉中进行了测试,采集了Venc = 80 cm/s和40 cm/s的包裹数据。通过视觉评估展开性能,并与无相位包裹的高Venc参考数据进行比较。对10例患有主动脉缩窄且具有临床Venc值和低Venc重建的患者进行校正,并由不知情的评审人员以0-3分进行评分。
使用所提出的方法,对于大多数临床相关的速度混叠水平,模拟数据都能完全展开。在主动脉的体内数据中,对于中度包裹的情况(Venc = 80 cm/s,峰值速度约为160 cm/s)能完全展开,而对于混叠更严重的数据集(Venc = 40 cm/s)仍存在残余混叠。在患者队列中,不知情的评审人员对标准和低Venc重建的评分均有提高(临床和低Venc数据集的平均评分分别提高了1.1分和2.2分)。
一种计算快速、完全自动化、易于使用且无参数的单步4D流数据展开方法被证明在大多数常见的临床速度混叠情况下有效。