Liu Tianming, Li Hai, Wong Kelvin, Tarokh Ashley, Guo Lei, Wong Stephen T C
Department of Radiology, The Methodist Hospital, Houston, TX, USA.
Neuroimage. 2007 Oct 15;38(1):114-23. doi: 10.1016/j.neuroimage.2007.07.002. Epub 2007 Jul 13.
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 non-WM 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.
我们提出了一种基于扩散张量成像（DTI）数据多通道融合的脑组织自动分割方法。该方法的依据是，基于DTI参数图像的独立组织分割为基于结构MRI数据的组织分割提供了互补的组织对比度信息。这在将结构数据与扩散数据融合时定义准确的组织图谱方面具有重要应用。在没有结构数据的情况下，基于DTI数据的组织分割提供了一种获取脑组织分割的替代方法。我们基于DTI数据进行组织分割的方法是利用单通道中存在的组织对比度将大脑分为两个部分。具体而言，由于脑脊液（CSF）中的表观扩散系数（ADC）值是灰质（GM）和白质（WM）的两倍多，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）图像用于将WM与非WM组织分开，因为高度定向的白质结构具有大得多的分数各向异性值。此外，还探索了其他用于分离组织的通道，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相与性能水平估计（STAPLE）算法的方法，该方法结合这两类图谱以获得CSF、GM和WM的完整组织分割图谱。提供了评估以证明我们方法的性能。还提供了将该方法应用于DTI数据和扰相梯度回波（SPGR）数据的脑组织分割及可变形配准的实验结果。
