Accelerating SENSE using compressed sensing

By Dong Liang, Bo Liu, Jiunjie Wang, Leslie Ying. Magnetic Resonance in Medicine, 2009

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A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction (2017, background): “These explicit redundancies can also be used to complement the sparse modelling of inherent redundancies [10], [11].”
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data (2019, unknown): “(11)  (u, v)= ‖u−v‖2 ‖u‖2 + ‖u−v‖1 ‖u‖1 , F I G U R E 2 The self-supervised learning scheme to train physics-guided deep learning without fully sampled data.”
Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss (2017, methodology): “Even combining parallel imaging and CS-MRI is studied to maximize the acceleration of acquisition [4].”
Low-rank and Sparse Matrix Decomposition for Accelerated Dynamic MRI with Separation of Background and Dynamic Components (2014, unknown): “proxhðyÞ 1⁄4 arg min x 1 2 jjy-xjj22 þ hðxÞ: [6]”