Abstract #2811
Model-based diffusion tensor denoising with tensor and FA smoothness constraints
Xi Peng 1 , Shanshan Wang 1 , Yuanyuan Liu 1 , and Dong Liang 1
1
Paul C. Lauterbur Research Centre for
Biomedical Imaging, Shenzhen Institutes of Advanced
Technology, Shenzhen, Guangdong, China
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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