Tangent vector-based gradient method with l12-regularization: Iterative half thresholding algorithm for CS-MRI

[Display omitted] •Tangent vector-based l12-regularization (thresholding technique) is proposed to solve the lp-regularization-based sparsity problem.•Proposed technique produces better reconstruction results for 2D Gaussian random sub-sampling for radial cardiac trajectory in terms of convergence.•The proposed method outperforms the standard CS reconstructions in our experiments with an improvement of 54.8% in RMSE and 14.3% in terms of PSNR. Object: This paper presents a new method using tangent vector-based l12-regularization for compressed sensing MR image reconstruction. The proposed method with l12-regularization is tested on four datasets: (i) 1-D sparse signal (ii) numerical cardiac phantom, (iii & iv) two sets of in-vivo cardiac MRI datasets acquired using 30 receiver coil elements with Cartesian and radial trajectories on 3T scanner. The results are compared with standard CS reconstruction, which utilizes l1-regularization. The experiments were also conducted for two different types of samplings: (i) cartesian sub-sampling and (ii) 2D random Gaussian sub-sampling. The quality of the reconstructed images is validated through Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR). The results show that the proposed method outperforms the standard CS reconstructions in our experiments with an improvement of 54.8% in RMSE and 14.3% in terms of PSNR. Moreover, the Gaussian random sub-sampling-based image reconstruction results are better than the Cartesian sub-sampling-based reconstruction results. The results show that the proposed method yields a good sparse signal approximation and superior convergence behavior, which implies a promising technique for the reconstruction of cardiac MR images as compared to the conventional CS algorithm.