PURPOSE: Brain tissue segmentation based on diffusion tensor magnetic resonance imaging (DT-MRI) data has been attempted by previous researchers. Due to inherent low spatial resolution of DT-MRI data, conventional methods suffered from partial volume averaging among the different types of tissues, which may result in inaccurate segmentation results. The purpose was to develop a new brain tissue segmentation method for DT-MRI data in which effect of the partial volume averaging is taken into account. METHODS: The method estimates the partial volume fractions of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) within each voxel using a maximum a posteriori probability principle, based on five DT parameters (three eigenvalues, apparent diffusion coefficient, and fractional anisotropy). The authors evaluated the performance of the proposed method quantitatively by using digital phantom data. Moreover, the authors applied the method to real DT-MRI data of the human brain, and compared the results with those of a conventional segmentation method. RESULTS: In the digital phantom experiments, the root mean square error in term of partial volume fraction with the method for WM, GM, and CSF were 0.137, 0.049, and 0.085, respectively. The volume overlap measures between the segmentation results and the ground truth of the digital phantom were more than 0.9 in all three tissue types, while those between the results by the conventional method and the ground truth ranged between 0.550 and 0.854. In visual comparisons for real DT-MRI, WM/GM/CSF regions estimated by the method were more similar to the corresponding regions depicted in the structural image than those estimated by the conventional method. CONCLUSIONS: The results of the digital phantom experiment and real DT-MRI data demonstrated that the method improved accuracy in estimation and segmentation of brain tissue on DT-MRI data over the conventional method. This method may be useful in evaluating the cortical and subcortical diffusivity in neurological diseases.
PURPOSE: Brain tissue segmentation based on diffusion tensor magnetic resonance imaging (DT-MRI) data has been attempted by previous researchers. Due to inherent low spatial resolution of DT-MRI data, conventional methods suffered from partial volume averaging among the different types of tissues, which may result in inaccurate segmentation results. The purpose was to develop a new brain tissue segmentation method for DT-MRI data in which effect of the partial volume averaging is taken into account. METHODS: The method estimates the partial volume fractions of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) within each voxel using a maximum a posteriori probability principle, based on five DT parameters (three eigenvalues, apparent diffusion coefficient, and fractional anisotropy). The authors evaluated the performance of the proposed method quantitatively by using digital phantom data. Moreover, the authors applied the method to real DT-MRI data of the human brain, and compared the results with those of a conventional segmentation method. RESULTS: In the digital phantom experiments, the root mean square error in term of partial volume fraction with the method for WM, GM, and CSF were 0.137, 0.049, and 0.085, respectively. The volume overlap measures between the segmentation results and the ground truth of the digital phantom were more than 0.9 in all three tissue types, while those between the results by the conventional method and the ground truth ranged between 0.550 and 0.854. In visual comparisons for real DT-MRI, WM/GM/CSF regions estimated by the method were more similar to the corresponding regions depicted in the structural image than those estimated by the conventional method. CONCLUSIONS: The results of the digital phantom experiment and real DT-MRI data demonstrated that the method improved accuracy in estimation and segmentation of brain tissue on DT-MRI data over the conventional method. This method may be useful in evaluating the cortical and subcortical diffusivity in neurological diseases.
Authors: Arash Nazeri; M Mallar Chakravarty; David J Rotenberg; Tarek K Rajji; Yogesh Rathi; Oleg V Michailovich; Aristotle N Voineskos Journal: J Neurosci Date: 2015-01-28 Impact factor: 6.167