Yanjie Zhu1, 2, Yin Wu1, 2, Ed X. Wu3, 4, Leslie Ying5, Dong Liang1, 2
1Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China; 2Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, China; 3Laboratory of Biomedical Imaging and Signal Processing; 4Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong; 5Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, WI, Milwaukee, United States
The joint sparsity constraint is integrated into the model-based method to improve the accuracy of direct diffusion tensor estimation from highly undersampled k-space data. The method, named model-based method with joint sparsity constraint (MB-JSC), effectively incorporates the prior information on the joint sparsity of different diffusion weighted images in solving the nonlinear equation of tensors. Experimental results demonstrate that the proposed method is able to estimate the diffusion tensors more accurately than the existing method when a high net reduction factor is used.