A Locally Linear Least Squares Method for Simultaneously Smoothing DWI Data and Estimating Diffusion Tensors
A Locally Linear Least Squares Method for Simultaneously Smoothing DWI Data and Estimating Diffusion Tensors
复制标题
同时平滑 DWI 数据和估计扩散张量的局部线性最小二乘法
DOI：
10.5405/jmbe.1174
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发表时间：
2013
影响因子：
2
通讯作者：
Dongrong Xu
中科院分区：
文献类型：
--
作者：
Xiaozheng Liu;Wei Liu;Guang Yang;W. Chen;Junming Zhu;Yongdi Zhou;B. Peterson;Dongrong Xu
磁共振弥散加权成像（MR-DWI）数据通常包含大量噪声和大量离群数据点，这些数据点可能会破坏弥散张量（DT）的准确估计。因此，原始MR-DWI数据通常必须在张量估计之前进行大量预处理。本研究提出了一种方法，用于从MR-DWI数据中重建DT场，该方法将原始MR-DWI数据的正则化和DT场的优化估计结合到单个步骤中。该方法使用局部加权线性最小二乘（LWLLS）估计相关的信息在每个体素的局部邻域内。它结合到线性最小二乘（LLS）框架的双边过滤器，分配不同的权重相邻体素根据其强度和相对距离。该方法有效地平滑MR-DWI数据，同时估计最佳张量。所提出的方法的性能进行了比较，传统的LLS估计张量使用模拟和真实世界的人类MR-DWI数据。模拟和真实世界的数据集都表明，所提出的方法大大优于传统的LLS方法，MR-DWI数据和张量估计的同时平滑以及这些程序的单独和顺序执行。
Magnetic resonance diffusion-weighted imaging (MR-DWI) data usually contain a great deal of noise and a significant number of outlier data points that can undermine the accurate estimation of diffusion tensors (DTs). Raw MR-DWI data therefore usually must undergo substantial preprocessing prior to tensor estimation. This study proposes an approach for the reconstruction of DT fields from MR-DWI data that combines into a single step the regularization of raw MR-DWI data and the optimized estimation of DT fields. The approach uses locally weighted linear least squares (LWLLS) estimation to correlate information within the local neighborhood of each voxel. It incorporates into the linear least squares (LLS) framework a bilateral filter which assigns different weights to neighbor voxels according to their intensities and relative distance. This method efficiently smoothes the MR-DWI data and estimates optimal tensors simultaneously. The performance of the proposed method was compared to that of traditional LLS estimation of tensors using both simulated and real-world human MR-DWI data. Both the simulated and real-world datasets demonstrated that the proposed method greatly outperforms the conventional LLS method and that the simultaneous smoothing of MR-DWI data and tensor estimation performs as well as the separate and sequential execution of these procedures.
影响因子：
3.3
作者：
Tuch, DS
通讯作者：
Tuch, DS