Double Smoothing Method-based Algorithm for MR Image Reconstruction with Partial Fourier Data

Undersampled MRI reconstruction techniques based on Compressed Sensing (CS) exploiting sparsity which is implicit in MR images can provide significant help in reducing the scan time during clinical period, but remains challenging due to the requirement of high reconstruction accuracy. A novel algorithm is developed and tested in vivo for solving the MR image reconstruction problem due to Nesterovs smoothing scheme and convex conic optimization.

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