Data to 7 Oct 2026

Plug and play methods for magnetic resonance imaging (long version)

arXiv 1903.08616 ↗First posted 20 Mar 2019cs.CVcited by 75

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Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic tool that provides excellent soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging modalities (e.g., CT or ultrasound), however, the data acquisition process for MRI is inherently slow, which motivates undersampling and thus drives the need for accurate, efficient reconstruction methods from undersampled datasets. In this article, we describe the use of "plug-and-play" (PnP) algorithms for MRI image recovery. We first describe the linearly approximated inverse problem encountered in MRI. Then we review several PnP methods, where the unifying commonality is to iteratively call a denoising subroutine as one step of a larger optimization-inspired algorithm. Next, we describe how the result of the PnP method can be interpreted as a solution to an equilibrium equation, allowing convergence analysis from the equilibrium perspective. Finally, we present illustrative examples of PnP methods applied to MRI image recovery.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Mar 2019.

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Citing papers per year: 2021: 11; 2022: 20; 2023: 15; 2024: 8; 2025: 13; 2026: 8.

75 papers.

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2022Transformers in Medical Imaging: A Surveycited by 245
2021Learning to Optimize: A Primer and A Benchmarkcited by 206
2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
2021Gradient Step Denoiser for convergent Plug-and-Playcited by 107
2021Bayesian imaging using Plug & Play priors: when Langevin meets Tweediecited by 96
2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priorscited by 93
2022Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularizationcited by 79
2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
2023Provable Probabilistic Imaging using Score-Based Generative Priorscited by 51
2022Model-Based Deep Learning: On the Intersection of Deep Learning and Optimizationcited by 50
2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 49
2021Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fieldscited by 48

38 more

2023Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inferencecited by 41
2022Imaging with Equivariant Deep Learningcited by 35
2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 29
2023Equivariant plug-and-play image reconstructioncited by 26
2021Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Conditioncited by 22
2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
2021Fixed-Point and Objective Convergence of Plug-and-Play Algorithmscited by 19
2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
2022On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descentcited by 14
2021Graph Signal Restoration Using Nested Deep Algorithm Unrollingcited by 13
2023Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysiscited by 10
2021Deformation-Compensated Learning for Image Reconstruction without Ground Truthcited by 9
2021MRI Reconstruction Using Deep Energy-Based Modelcited by 8
2022Deep Model-Based Architectures for Inverse Problems under Mismatched Priorscited by 8
2023Block Coordinate Plug-and-Play Methods for Blind Inverse Problemscited by 8
2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
2021Deep Algorithm Unrolling for Biomedical Imagingcited by 6
2022A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoiserscited by 5
2022Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-trainingcited by 5
2022CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mappingcited by 5
2023Convergent plug-and-play with proximal denoiser and unconstrained regularization parametercited by 5
2024Plug-and-Play Priors as a Score-Based Methodcited by 5
2022Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariancecited by 4
2022Deep network series for large-scale high-dynamic range imagingcited by 4
2025PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRIcited by 4
2022MA-RECON: Mask-aware deep-neural-network for robust fast MRI k-space interpolationcited by 3
2023Learning Task-Specific Strategies for Accelerated MRIcited by 3
2023PRISTA-Net: Deep Iterative Shrinkage Thresholding Network for Coded Diffraction Patterns Phase Retrievalcited by 3
2023Single-Shot Plug-and-Play Methods for Inverse Problemscited by 3
2023Convergence of Nonconvex PnP-ADMM with MMSE Denoiserscited by 3
2023An ℓ1-Plug-and-Play Approach for MPI Using a Zero Shot Denoiser with Evaluation on the 3D Open MPI Datasetcited by 3
2025PSI3D: Plug-and-Play 3D Stochastic Inference with Slice-wise Latent Diffusion Priorcited by 3
2026Bayesian Uncertainty-Aware MRI Reconstructioncited by 3
2022Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernelscited by 2
2024A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modelingcited by 2
2024LoFi: Neural Local Fields for Scalable Image Reconstructioncited by 2
2024Fast MRI for All: Bridging Access Gaps by Training without Raw Datacited by 2
2025An unsupervised method for MRI recovery: Deep image prior with structured sparsitycited by 2

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