Collier Quinten, Veraart Jelle, Jeurissen Ben, den Dekker Arnold J, Sijbers Jan
iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands.
Magn Reson Med. 2015 Jun;73(6):2174-84. doi: 10.1002/mrm.25351. Epub 2014 Jul 1.
Diffusion-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.
In 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.
Both 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.
The 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.
扩散加权磁共振成像存在生理噪声，如由运动或系统不稳定引起的伪影。因此，需要强大的扩散参数估计技术。过去已经提出了几种技术，包括RESTORE和iRESTORE（Chang等人，《磁共振医学》，2005年；53:1088 - 1095；Chang等人，《磁共振医学》，2012年；68:1654 - 1663）。然而，这些技术基于非线性估计器，因此计算量很大。
在这项工作中，我们提出了一种新的、强大的、迭代加权线性最小二乘（IRLLS）估计器。IRLLS在扩散加权磁共振图像中对体素逐个进行异常值识别，它利用数据分布的自然偏度，对信号高强化和信号丢失都更加敏感。
进行了模拟和实际数据实验，以将IRLLS与其他现有技术进行比较。虽然IRLLS在准确性或精度上没有显著损失，但事实证明它比RESTORE和iRESTORE都要快得多。此外，在考虑噪声水平高估或信噪比低的情况下，IRLLS被证明更加稳健。
大幅缩短的计算时间，再加上增强的稳健性和准确性，使IRLLS成为当前用于稳健估计扩散加权磁共振参数的现有技术的一种实用且可靠的替代方法。
