Brain tissue segmentation based on DTI data
Brain tissue segmentation based on DTI data
复制标题
DOI：
10.1016/j.neuroimage.2007.07.002
复制
发表时间：
2007-10-15
期刊：
影响因子：
5.7
通讯作者：
Wong, Stephen T. C.
中科院分区：
文献类型：
--
作者：
Liu, Tianming;Li, Hai;Wong, Stephen T. C.
提出了一种基于多通道扩散张量成像（DTI）数据融合的脑组织自动分割方法。该方法的动机是基于DTI参数图像的独立组织分割提供了组织对比度的补充信息，基于结构MRI数据的组织分割的证据。这在将结构数据与扩散数据融合时定义精确的组织图方面具有重要的应用。在缺乏结构数据的情况下，基于DTI数据的组织分割提供了获得脑组织分割的替代手段。基于DTI数据的组织分割方法是利用单个通道中存在的组织对比度将大脑分为两个部分。具体地说，由于脑脊液（CSF）中的表观扩散系数（ADC）值是灰质（GM）和白色物质（WM）的两倍以上，我们使用ADC图像来区分CSF和非CSF组织。此外，分数各向异性（FA）图像用于分离WM与非WM组织，因为高度定向的白色物质结构具有大得多的分数各向异性值。此外，其他渠道来分离组织进行了探索，如张量的特征值，相对各向异性（RA），和体积比（VR）。我们开发了一种基于同时真实和性能水平估计（STAPLE）算法的方法，该算法结合了这两类图，以获得CSF，GM和WM的完整组织分割图。提供评估，以证明我们的方法的性能。将这种方法应用于脑组织分割和变形登记的DTI数据和破坏梯度回波（SPGR）数据的实验结果也提供。(c)2007年爱思唯尔公司All rights reserved.
We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from nonWM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided. (c) 2007 Elsevier Inc. All rights reserved.