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Abstract #1412

A Novel Hybrid Total Variation Minimization Method to MRI Reconstruction from Highly Undersampled Data

Hongyu Li1, Yong Wang2, Dong Liang3, and Leslie Ying1

1Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States, 2School of Electronic Engineering, Xidian University, Xi'an, People's Republic of China, 3Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, People's Republic of China

This abstract presents a novel hybrid total variation minimization algorithm to reconstruct MR images from reduced measurements. The method combines the benefits of both L1 and homotopic L0 minimization algorithms for sparse signal reconstruction in the sense that substantially fewer measurements are needed for exact reconstruction. The algorithm minimizes the conventional total variation when the gradient is small, and minimizes the L0 of gradient when the gradient is large. An auto-adaptive threshold determines the transition between L1 and L0 of the gradients. The experimental results show the proposed algorithm outperforms either L1 or homotopic L0 minimization when the same reduction factor is used.

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