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基于加权最小二乘法的 4D 流 MRI 无散约束相位解缠和去噪

Divergence-Free Constrained Phase Unwrapping and Denoising for 4D Flow MRI Using Weighted Least-Squares.

出版信息

IEEE Trans Med Imaging. 2021 Dec;40(12):3389-3399. doi: 10.1109/TMI.2021.3086331. Epub 2021 Nov 30.

Abstract

A novel divergence-free constrained phase unwrapping method was proposed and evaluated for 4D flow MRI. The unwrapped phase field was obtained by integrating the phase variations estimated from the wrapped phase data using weighted least-squares. The divergence-free constraint for incompressible blood flow was incorporated to regulate and denoise the resulting phase field. The proposed method was tested on synthetic phase data of left ventricular flow and in vitro 4D flow measurement of Poiseuille flow. The method was additionally applied to in vivo 4D flow measurements in the thoracic aorta from 30 human subjects. The performance of the proposed method was compared to the state-of-the-art 4D single-step Laplacian algorithm. The synthetic phase data were completely unwrapped by the proposed method for all the cases with velocity encoding (venc) as low as 20% of the maximum velocity and signal-to-noise ratio as low as 5. The in vitro Poiseuille flow data were completely unwrapped with a 60% increase in the velocity-to-noise ratio. For the in-vivo aortic datasets with venc ratio less than 0.4, the proposed method significantly improved the success rate by as much as 40% and reduced the velocity error levels by a factor of 10 compared to the state-of-the-art method. The divergence-free constrained method exhibits reliability and robustness on phase unwrapping and shows improved accuracy of velocity and hemodynamic quantities by unwrapping the low-venc 4D flow MRI data.

摘要

一种新的无散约束相位解缠方法被提出并应用于 4D 血流 MRI。通过对包裹相位数据中的相位变化进行加权最小二乘估计,得到解包裹相位场。将不可压缩血流的无散约束纳入其中,以调节和降噪生成的相位场。该方法在左心室血流的合成相位数据和泊肃叶流的体外 4D 流测量中进行了测试。此外,该方法还应用于 30 名人类受试者的胸主动脉的体内 4D 流测量。与最先进的 4D 单步拉普拉斯算法相比,评估了该方法的性能。对于所有情况下的合成相位数据,当速度编码(venc)低至最大速度的 20%且信噪比低至 5 时,该方法可以完全解缠相位。在体外泊肃叶流数据中,速度信噪比提高了 60%。对于 venc 比小于 0.4 的体内主动脉数据集,与最先进的方法相比,该方法显著提高了成功率,最大提高了 40%,并且降低了速度误差水平达 10 倍。无散约束方法在相位解缠方面具有可靠性和鲁棒性,通过解缠低 venc 的 4D 流 MRI 数据,提高了速度和血流动力学参数的准确性。

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本文引用的文献

1
Blood flow imaging by optimal matching of computational fluid dynamics to 4D-flow data.
Magn Reson Med. 2020 Oct;84(4):2231-2245. doi: 10.1002/mrm.28269. Epub 2020 Apr 8.
2
Fully automated 3D aortic segmentation of 4D flow MRI for hemodynamic analysis using deep learning.
Magn Reson Med. 2020 Oct;84(4):2204-2218. doi: 10.1002/mrm.28257. Epub 2020 Mar 13.
3
4D Flow MRI Pressure Estimation Using Velocity Measurement-Error-Based Weighted Least-Squares.
IEEE Trans Med Imaging. 2020 May;39(5):1668-1680. doi: 10.1109/TMI.2019.2954697. Epub 2019 Nov 21.
4
Multi-modality cerebral aneurysm haemodynamic analysis: 4D flow MRI, volumetric particle velocimetry and computational fluid dynamics.
J R Soc Interface. 2019 Sep 27;16(158):20190465. doi: 10.1098/rsif.2019.0465. Epub 2019 Sep 11.
5
Standardized Evaluation of Cerebral Arteriovenous Malformations Using Flow Distribution Network Graphs and Dual-venc 4D Flow MRI.
J Magn Reson Imaging. 2019 Dec;50(6):1718-1730. doi: 10.1002/jmri.26784. Epub 2019 May 9.
6
Optimal Dual-VENC Unwrapping in Phase-Contrast MRI.
IEEE Trans Med Imaging. 2019 May;38(5):1263-1270. doi: 10.1109/TMI.2018.2882553. Epub 2018 Nov 21.
7
A Bayesian approach for 4D flow imaging of aortic valve in a single breath-hold.
Magn Reson Med. 2019 Feb;81(2):811-824. doi: 10.1002/mrm.27386. Epub 2018 Sep 28.
8
4D-Flow MRI: Technique and Applications.
Rofo. 2018 Nov;190(11):1025-1035. doi: 10.1055/a-0647-2021. Epub 2018 Aug 13.
9
Velocity reconstruction with nonconvex optimization for low-velocity-encoding phase-contrast MRI.
Magn Reson Med. 2018 Jul;80(1):42-52. doi: 10.1002/mrm.26997. Epub 2017 Nov 11.
10
Assessment of methodologies to calculate intraventricular pressure differences in computational models and patients.
Med Biol Eng Comput. 2018 Mar;56(3):469-481. doi: 10.1007/s11517-017-1704-0. Epub 2017 Aug 16.

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