11 March 2008 Spatial normalization of diffusion tensor images based on anisotropic segmentation
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A comprehensive framework is proposed for the spatial normalization of diffusion tensor (DT) brain images using tensor-derived tissue attributes. In this framework, the brain tissues are first classified into three categories: the white matter (WM), the gray matter (GM), and the cerebral-spinal fluid (CSF) using the anisotropy and diffusivity information derived from the full tensor. The tissue attributes obtained from this anisotropic segmentation are then incorporated into a very-high-dimensional elastic registration method to produce a spatial deformation field. Finally, the rotational component in the deformation field, together with the estimated underlying fiber direction, is used to determine an appropriate tensor reorientation. This framework has been assessed quantitatively and qualitatively based on a sequence of experiments. A simulated experiment has been performed to evaluate the accuracy of the spatial warping by examining the variation between deformation fields. To verify the tensor reorientation, especially, in the anisotropic microstructures of WM fiber tissues, an experiment has been designed to compare the fiber tracts generated from the DT template and the normalized DT subjects in some regions of interest (ROIs). Finally, this method has been applied to spatially normalize 31 subjects to a common space, the case in which there exist large deformations between subjects and the existing approaches are normally difficult to achieve satisfactory results. The average across the individual normalized DT images shows a significant improvement in signal-to-noise ratio (SNR).
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Jinzhong Yang, Jinzhong Yang, Dinggang Shen, Dinggang Shen, Chandan Misra, Chandan Misra, Xiaoying Wu, Xiaoying Wu, Susan Resnick, Susan Resnick, Christos Davatzikos, Christos Davatzikos, Ragini Verma, Ragini Verma, } "Spatial normalization of diffusion tensor images based on anisotropic segmentation", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69140L (11 March 2008); doi: 10.1117/12.769846; https://doi.org/10.1117/12.769846

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