Reference-driven method for MR image reconstruction based on wavelet sparsity and nonlocal total variation
Reference-driven method for MR image reconstruction based on wavelet sparsity and nonlocal total variation

A novel reference-driven method for MR image reconstruction based on wavelet sparsity and nonlocal total variation (NLTV) is proposed. Utilizing the sparsity of the difference image between the target image and the motion-compensated reference image in wavelet transform domain, the proposed method does not need to estimate contrast changes and therefore increases computational efficiency. Additionally, NLTV regularization is applied to preserve image details and features without blocky effects. An efficient alternating iterative algorithm is used to estimate motion effects and reconstruct the difference image. Experimental results demonstrate that the proposed method can significantly reduce sampling rate or improve the quality of the reconstructed image alternatively.
A novel reference-driven method for MR image reconstruction based on wavelet sparsity and nonlocal total variation (NLTV) is proposed. Utilizing the sparsity of the difference image between the target image and the motion-compensated reference image in wavelet transform domain, the proposed method does not need to estimate contrast changes and therefore increases computational efficiency. Additionally, NLTV regularization is applied to preserve image details and features without blocky effects. An efficient alternating iterative algorithm is used to estimate motion effects and reconstruct the difference image. Experimental results demonstrate that the proposed method can significantly reduce sampling rate or improve the quality of the reconstructed image alternatively.