RESTORE: Robust estimation of tensors by outlier rejection
RESTORE: Robust estimation of tensors by outlier rejection
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DOI：
10.1002/mrm.20426
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发表时间：
2005-05-01
影响因子：
3.3
通讯作者：
Pierpaoli, C
中科院分区：
文献类型：
--
作者：
Chang, LC;Jones, DK;Pierpaoli, C
扩散加权成像（DWI）中的信号变异性受到热噪声以及空间和时间变化伪影（如受试者运动和心脏搏动）的影响。在本文中，DWI伪影估计张量值，如跟踪和分数各向异性的影响，使用蒙特卡罗模拟进行了分析。提出了一种新的鲁棒扩散张量估计方法RESTORE（forrobustestimationoftensors by outlierrejection）。该方法使用迭代重加权最小二乘回归来识别潜在的离群值，并随后排除它们。从模拟和临床扩散数据集的结果表明，RESTORE方法改善张量估计相比，常用的线性和非线性最小二乘张量拟合方法和最近提出的方法的基础上的Geman-McClure M-估计。RESTORE方法可能消除DWI采集中对心脏门控的需求，并且应适用于使用单变量或多变量回归将MRI数据拟合到模型的其他MR成像技术。2005年出版Wiley-Liss，Inc.（匕首）
Signal variability in diffusion weighted imaging (DWI) is influenced by both thermal noise and spatially and temporally varying artifacts such as subject motion and cardiac pulsation. In this paper, the effects of DWI artifacts on estimated tensor values, such as trace and fractional anisotropy, are analyzed using Monte Carlo simulations. A novel approach for robust diffusion tensor estimation, called RESTORE (for robust estimation of tensors by outlier rejection), is proposed. This method uses iteratively reweighted least-squares regression to identify potential outliers and subsequently exclude them. Results from both simulated and clinical diffusion data sets indicate that the RESTORE method improves tensor estimation compared to the commonly used linear and nonlinear least-squares tensor fitting methods and a recently proposed method based on the Geman-McClure M-estimator. The RESTORE method could potentially remove the need for cardiac gating in DWI acquisitions and should be applicable to other MR imaging techniques that use univariate or multivariate regression to fit MRI data to a model. Published 2005 Wiley-Liss, Inc.(dagger)