Diffusion tensor imaging: Structural adaptive smoothing
Diffusion tensor imaging: Structural adaptive smoothing
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DOI：
10.1016/j.neuroimage.2007.10.024
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发表时间：
2008-02-15
期刊：
影响因子：
5.7
通讯作者：
Voss, Henning U.
中科院分区：
文献类型：
--
作者：
Tabelow, Karsten;Polzehl, Joerg;Voss, Henning U.
弥散张量成像（DTI）数据的特征在于高噪声水平。因此，用于纤维跟踪的各向异性指数或主扩散方向等量的估计误差相对较大，并且可能显著混淆DTI在临床或神经科学应用中的准确性。除了脉冲序列优化之外，通过平滑数据来降低噪声可以作为增加DTI准确性的补充方法。在这里，我们提出了一个各向异性的结构自适应平滑程序，这是基于的扩展分离方法，并保留在DTI中看到的结构和它们的不同大小和形状。它适用于人工体模数据和大脑扫描。我们表明，这种方法显着提高了质量的扩散张量的估计，通过双方的偏见和方差减少，因此，使一个要么减少扫描的数量或增强输入的后续分析，如纤维跟踪。(C)2007年爱思唯尔公司All rights reserved.
Diffusion Tensor Imaging (DTI) data is characterized by a high noise level. Thus, estimation errors of quantities like anisotropy indices or the main diffusion direction used for fiber tracking are relatively large and may significantly confound the accuracy of DTI in clinical or neuroscience applications. Besides pulse sequence optimization, noise reduction by smoothing the data can be pursued as a complementary approach to increase the accuracy of DTI. Here, we suggest an anisotropic structural adaptive smoothing procedure, which is based on the Propagation-Separation method and preserves the structures seen in DTI and their different sizes and shapes. It is applied to artificial phantom data and a brain scan. We show that this method significantly improves the quality of the estimate of the diffusion tensor, by means of both bias and variance reduction, and hence enables one either to reduce the number of scans or to enhance the input for subsequent analysis such as fiber tracking. (C) 2007 Elsevier Inc. All rights reserved.