An efficient algorithm for compressed MR imaging using total variation and wavelets

By Shiqian Ma, Wotao Yin, Yin Zhang, Amit Chakraborty. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

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Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data (2009, methodology): “Both phantoms and anatomical images were reconstructed well with as little as 20% sampling, using patterns such as spirals and randomly-positioned lines.”
MoDL: Model-Based Deep Learning Architecture for Inverse Problems (2017, methodology): “We also compare the performance of the proposed MoDL framework with compressed sensing based technique that utilizes total variation regularization acronym-ed as CSTV [5].”
MR Image Reconstruction From Highly Undersampled k-Space Data by Dictionary Learning (2011, methodology): “Ma et al. [ 22 ] present an algorithm based on an iterative operator-splitting framework that exploits fast wavelet and Fourier transforms to enable fast reconstruction.”
A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data (2010, methodology): “In tests 2 and 3, we compared RecPF with OS [35] on solving problem (8) with both TV and `1 terms.”