- ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA
- Structured Low-Rank Algorithms: Theory, Magnetic Resonance Applications, and Links to Machine Learning
- Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization
- PALMNUT: An Enhanced Proximal Alternating Linearized Minimization Algorithm with Application to Separate Regularization of Magnitude and Phase
- Motion compensated generalized reconstruction for free-breathing dynamic contrast-enhanced MRI
- Physics-based reconstruction methods for magnetic resonance imaging
- Efficient Regularized Field Map Estimation in 3D MRI
- Improved simultaneous multislice cardiac MRI using readout concatenated k-space SPIRiT (ROCK-SPIRiT)
- Plug-and-Play Priors for Model Based Reconstruction
- Plug-and-play methods provably converge with properly trained denoisers
- ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
- Deep ADMM-Net for Compressive Sensing MRI
- MoDL: Model Based Deep Learning Architecture for Inverse Problems
- Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks
- Noise2Noise: Learning Image Restoration without Clean Data
- J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
- The Little Engine that Could: Regularization by Denoising (RED)
- Eigenvector-based SPIRiT Parallel MR Imaging Reconstruction based on ℓp pseudo-norm Joint Total Variation
- Noise2Noise: Learning Image Restoration without Clean Data
- Noise2Void - Learning Denoising from Single Noisy Images
- Sparse Reconstruction Techniques in Magnetic Resonance Imaging
- Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction
- Compressed sensing MRI: a review from signal processing perspective
- Sparse Reconstruction Techniques in MRI: Methods, Applications, and Challenges to Clinical Adoption
- AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis
- Accelerating magnetic resonance imaging via deep learning
- Deep residual learning for compressed sensing MRI
- Deep Learning–based Method for Denoising and Image Enhancement in Low-Field MRI
- Deflated preconditioned Conjugate Gradient methods for noise filtering of low-field MR images
- A Dictionary Learning Approach for Joint Reconstruction and Denoising in Low Field Magnetic Resonance Imaging
- Low-Field MRI: How Low Can We Go? A Fresh View on an Old Debate
- Deep Learning in MR Motion Correction: a Brief Review and a New Motion Simulation Tool (view2Dmotion)
- Matlab code of view2Dmotion (https://github.com/Yonsei-MILab/MRI-Motion-Artifact-Simulation-Tool)
