Direct diffusion tensor estimation using joint sparsity constraint without image reconstruction

Abstract

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.