Brain Tissue Segmentation Based on Diffusion MRI U

Authors: PewThian Yap Yong Zhang Dinggang Shen

Publish Date: 2015/10/5

Volume: , Issue: , Pages: 132-139

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Abstract

We present a method for automated brain tissue segmentation based on diffusion MRI This provides information that is complementary to structural MRI and facilitates fusion of information between the two imaging modalities Unlike existing segmentation approaches that are based on diffusion tensor imaging DTI our method explicitly models the coexistence of various diffusion compartments within each voxel owing to different tissue types and different fiber orientations This results in improved segmentation in regions with white matter crossings and in regions susceptible to partial volume effects For each voxel we tease apart possible signal contributions from white matter WM gray matter GM and cerebrospinal fluid CSF with the help of diffusion exemplars which are representative signals associated with each tissue type Each voxel is then classified by determining which of the WM GM or CSF diffusion exemplar groups explains the signal better with the least fitting residual Fitting is performed using ℓ0 sparsegroup approximation circumventing various reported limitations of ℓ1 fitting In addition to promote spatial regularity we introduce a smoothing technique that is based on ℓ0 gradient minimization which can be viewed as the ℓ0 version of total variation TV smoothing Compared with the latter our smoothing technique which also incorporates multichannel WM GM and CSF concurrent smoothing yields marked improvement in preserving boundary contrast and consequently reduces segmentation bias caused by smoothing at tissue boundaries The results produced by our method are in good agreement with segmentation based on T1weighted images

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