Iterative reweighted linear least squares for accurate, fast, and robust estimation of diffusion magnetic resonance parameters
Iterative reweighted linear least squares for accurate, fast, and robust estimation of diffusion magnetic resonance parameters
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DOI：
10.1002/mrm.25351
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发表时间：
2015-06-01
影响因子：
3.3
通讯作者：
Sijbers, Jan
中科院分区：
文献类型：
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作者：
Collier, Quinten;Veraart, Jelle;Sijbers, Jan
目的磁共振弥散加权成像受生理噪声的影响，如运动或系统不稳定引起的伪影。因此，需要稳健的扩散参数估计技术。在过去，已经提出了几种技术，包括RESTORE和iRESTORE（Chang等人，Magn Reson Med 2005; 53：1088-1095; Chang等人，Magn Reson Med 2012; 68：1654-1663）。然而，这些技术是基于非线性估计，因此计算密集型。MethodIn这项工作中，我们提出了一个新的，强大的，迭代加权线性最小二乘（IRLLS）估计。IRLLS执行扩散加权磁共振图像中的离群值的体素识别，它利用自然的数据分布的偏度变得更敏感的信号hyperintensities和信号dropouts.ResultsBoth模拟和真实的数据实验进行比较IRLLS与其他国家的最先进的技术。虽然IRLLS在准确性或精度方面没有显着损失，但它被证明比RESTORE和iRESTORE快得多。此外，IRLLS被证明是更强大的时候，考虑到噪声水平的高估或当信噪比是low.ConclusionThe大大缩短的计算时间，结合增加的鲁棒性和准确性，使IRLLS一个实用和可靠的替代目前国家的最先进的技术，扩散加权磁共振参数的鲁棒估计。Magn Reson Med 73：2174-2184，2015。(c)2014 Wiley Periodicals，Inc.
PurposeDiffusion-weighted magnetic resonance imaging suffers from physiological noise, such as artifacts caused by motion or system instabilities. Therefore, there is a need for robust diffusion parameter estimation techniques. In the past, several techniques have been proposed, including RESTORE and iRESTORE (Chang et al. Magn Reson Med 2005; 53:1088-1095; Chang et al. Magn Reson Med 2012; 68:1654-1663). However, these techniques are based on nonlinear estimators and are consequently computationally intensive.MethodIn this work, we present a new, robust, iteratively reweighted linear least squares (IRLLS) estimator. IRLLS performs a voxel-wise identification of outliers in diffusion-weighted magnetic resonance images, where it exploits the natural skewness of the data distribution to become more sensitive to both signal hyperintensities and signal dropouts.ResultsBoth simulations and real data experiments were conducted to compare IRLLS with other state-of-the-art techniques. While IRLLS showed no significant loss in accuracy or precision, it proved to be substantially faster than both RESTORE and iRESTORE. In addition, IRLLS proved to be even more robust when considering the overestimation of the noise level or when the signal-to-noise ratio is low.ConclusionThe substantially shortened calculation time in combination with the increased robustness and accuracy, make IRLLS a practical and reliable alternative to current state-of-the-art techniques for the robust estimation of diffusion-weighted magnetic resonance parameters. Magn Reson Med 73:2174-2184, 2015. (c) 2014 Wiley Periodicals, Inc.