DWI filtering using joint information for DTI and HARDI
DWI filtering using joint information for DTI and HARDI
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DOI：
10.1016/j.media.2009.11.001
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发表时间：
2010-04-01
影响因子：
10.9
通讯作者：
Aja-Fernandez, Santiago
中科院分区：
文献类型：
--
作者：
Tristan-Vega, Antonio;Aja-Fernandez, Santiago
在最近的文献中，在估计扩散张量或其他纤维方向分布函数（ODE）之前对扩散加权图像（DWI）进行滤波已被证明是至关重要的。更确切地说，已经证明，在没有先前滤波阶段的情况下对扩散张量的估计引起不能通过张量场的进一步正则化来恢复的误差。许多方法已经打算克服这个问题，他们中的大多数分别基于每个DWI梯度图像的恢复。在本文中，我们提出了一种方法，以利用联合信息的DWI卷，即，所有DWI通道给出的信息之和加上它们之间的相关性。通过这种方式，所有梯度图像都可以利用它们共享的一阶和二阶信息一起进行滤波。我们适应这种方法的两个过滤器，即线性最小均方误差（LMMSE）和无偏非局部均值（UNLM）。这些新的过滤器进行了测试，在各种各样的合成和真实的数据显示的方便的新方法，特别是高角分辨率扩散成像（HARDI）。在所提出的技术中，联合LMMSE被证明是一种非常有吸引力的方法，因为它显示出类似于UNLM的精度（在某些情况下甚至更好），计算量要轻得多。(C)2009 Elsevier B.V.保留所有权利。
The filtering of the Diffusion Weighted Images (DWI) prior to the estimation of the diffusion tensor or other fiber Orientation Distribution Functions (ODE) has been proved to be of paramount importance in the recent literature. More precisely, it has been evidenced that the estimation of the diffusion tensor without a previous filtering stage induces errors which cannot be recovered by further regularization of the tensor field. A number of approaches have been intended to overcome this problem, most of them based on the restoration of each DWI gradient image separately. In this paper we propose a methodology to take advantage of the joint information in the DWI volumes, i.e., the sum of the information given by all DWI channels plus the correlations between them. This way, all the gradient images are filtered together exploiting the first and second order information they share. We adapt this methodology to two filters, namely the Linear Minimum Mean Squared Error (LMMSE) and the Unbiased Non-Local Means (UNLM). These new filters are tested over a wide variety of synthetic and real data showing the convenience of the new approach, especially for High Angular Resolution Diffusion Imaging (HARDI). Among the techniques presented, the joint LMMSE is proved a very attractive approach, since it shows an accuracy similar to UNLM (or even better in some situations) with a much lighter computational load. (C) 2009 Elsevier B.V. All rights reserved.