In this abstract, we introduce a new multi-tensor regularization and denoising technique based on Expectation-Maximization framework. To reduce filtering blurring effect and preserve sharp edges, we incorporated anisotropic regularization weight to the framework. We also utilize a tensor similarity metric, made from a quaternion representation, to improve the regularization and preserve tensor characteristics. Finally, we evaluate and compare filtering methods using a diffusion MRI synthetic phantom and in-vivo acquisition.
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