Model-based diffusion tensor denoising with tensor and FA smoothness constraints

Low SNR is a significant problem in diffusion tensor imaging. Recent methods using sparse or low rank models usually denoise in image space. One has to go through an estimation chain (i.e., image tensor eigen-valueFA) to obtain the FA map, which may cause error propagation. This work proposes to use the model-based method for DTI denoising. Notably, we creatively penalize the non-smoothness of the tensor and the nonlinear FA simultaneously. To enable this, we calculate FA values from elements of the tensors directly without computing the eigen-values. Experiments were conducted and show promising results in heavy noise case.

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