no code implementations • 24 Aug 2019 • Shan-Shan Wang, Yanxia Chen, Taohui Xiao, Ziwen Ke, Qiegen Liu, Hairong Zheng
In comparison with state-of-the-art methods, extensive experiments show that our method achieves consistent better reconstruction performance on the MRI reconstruction in terms of three quantitative metrics (PSNR, SSIM and HFEN) under different undersamling patterns and acceleration factors.
no code implementations • 6 Aug 2019 • Yanxia Chen, Taohui Xiao, Cheng Li, Qiegen Liu, Shan-Shan Wang
Three main contributions have been made: a de-aliasing reconstruction model was proposed to accelerate parallel MR imaging with deep learning exploring both spatial redundancy and multi-coil correlations; a split Bregman iteration algorithm was developed to solve the model efficiently; and unlike most existing parallel imaging methods which rely on the accuracy of the estimated multi-coil sensitivity, the proposed method can perform parallel reconstruction from undersampled data without explicit sensitivity calculation.