MRI Reconstruction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

2 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 198 papers with code (441 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction
  30 Nov 2021 7 repositories listed
  Machine Learning methods can learn how to reconstruct Magnetic Resonance Images and thereby accelerate acquisition, which is of paramount importance to the clinical workflow.
• Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
  14 Aug 2020 5 repositories listed
  Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently.
• Accelerated MRI with Un-trained Neural Networks
  6 Jul 2020 4 repositories listed
  Convolutional Neural Networks (CNNs) are highly effective for image reconstruction problems.
• DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models
  9 May 2024 3 repositories listed
  Moreover, virtual binary modal masks are utilized to refine the range of values in k-space data through highly adaptive center windows, which allows the model to focus its attention more efficiently.
• Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction
  18 Nov 2021 3 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 2 pointer-only (licence)
  Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours.
• Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
  9 Dec 2020 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
• XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge
  15 Oct 2020 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We present a new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data.
• End-to-End Variational Networks for Accelerated MRI Reconstruction
  14 Apr 2020 3 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer…
• DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstruction
  14 Jan 2025 2 repositories listed
  To address these, we propose a dual-domain hierarchical Mamba for MRI reconstruction from the following perspectives: (1) We pioneer vision Mamba in k-space learning.
• SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models
  3 Oct 2023 2 repositories listed
  However, diffusion models require careful tuning of inference hyperparameters on a validation set and are still sensitive to distribution shifts during testing.
• Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms
  21 Aug 2023 2 repositories listed Syntology ran 9 of 11 samples · 2 unverified · 11 pointer-only (licence)
  We propose to learn non-convex regularizers with a prescribed upper bound on their weak-convexity modulus.
• Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentation
  4 Aug 2023 2 repositories listed
  Purpose: In this work, we present a workflow to construct generic and robust generative image priors from magnitude-only images.
• Generative AI for Medical Imaging: extending the MONAI Framework
  27 Jul 2023 2 repositories listed Syntology ran 3 of 6 samples · 3 unverified
  We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and…
• MRI Recovery with Self-Calibrated Denoisers without Fully-Sampled Data
  25 Apr 2023 2 repositories listed
  We present a self-supervised image reconstruction method, termed ReSiDe, capable of recovering images solely from undersampled data.
• SMUG: Towards robust MRI reconstruction by smoothed unrolling
  14 Mar 2023 2 repositories listed
  To address this problem, we propose a novel image reconstruction framework, termed SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust…
• Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems
  10 Mar 2023 2 repositories listed Syntology ran 16 of 27 samples · 11 unverified · 27 pointer-only (licence)
  In this study, we propose a novel and efficient diffusion sampling strategy that synergistically combines the diffusion sampling and Krylov subspace methods.
• A Neural-Network-Based Convex Regularizer for Inverse Problems
  22 Nov 2022 2 repositories listed
  The emergence of deep-learning-based methods to solve image-reconstruction problems has enabled a significant increase in reconstruction quality.
• T2LR-Net: An unrolling network learning transformed tensor low-rank prior for dynamic MR image reconstruction
  8 Sep 2022 2 repositories listed
  Subsequently, we present a dynamic MRI reconstruction model based on UTNN and devise an efficient iterative optimization algorithm using ADMM, which is finally unfolded into the proposed T2LR-Net.
• Monarch: Expressive Structured Matrices for Efficient and Accurate Training
  1 Apr 2022 2 repositories listed Syntology ran 2 of 15 samples · 13 unverified
  To address these issues, we propose a class of matrices (Monarch) that is hardware-efficient (they are parameterized as products of two block-diagonal matrices for better hardware utilization) and expressive (they can…
• HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction
  15 Mar 2022 2 repositories listed Syntology ran 5 of 5 samples · 0 unverified
  These models split input images into non-overlapping patches, embed the patches into lower-dimensional tokens and utilize a self-attention mechanism that does not suffer from the aforementioned weaknesses of…
• Swin Transformer for Fast MRI
  10 Jan 2022 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.
• Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results
  16 Sep 2021 2 repositories listed
  We demonstrate this phenomenon for inverse problem solvers and show how their biased performance stems from hidden data preprocessing pipelines.
• Data augmentation for deep learning based accelerated MRI reconstruction with limited data
  28 Jun 2021 2 repositories listed
  Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks.
• Is good old GRAPPA dead?
  1 Jun 2021 2 repositories listed
  We perform a qualitative analysis of performance of XPDNet, a state-of-the-art deep learning approach for MRI reconstruction, compared to GRAPPA, a classical approach.
• Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks
  6 Jan 2021 2 repositories listed
  In this paper, we explore a novel strategy of using a hypernetwork to generate the parameters of a separate reconstruction network as a function of the regularization weight(s), resulting in a regularization-agnostic…
• Learning Multiscale Convolutional Dictionaries for Image Reconstruction
  25 Nov 2020 2 repositories listed
  To close the performance gap, we thus propose a multiscale convolutional dictionary structure.
• Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data
  16 Dec 2019 2 repositories listed
  Results: Results on five different knee sequences at acceleration rate of 4 shows that proposed self-supervised approach performs closely with supervised learning, while significantly outperforming conventional…
• Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
  31 May 2017 2 repositories listed
  A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality.
• MDPG: Multi-domain Diffusion Prior Guidance for MRI Reconstruction
  30 Jun 2025 1 repository listed
  Latent diffusion models (LDMs) yield both compact and detailed prior knowledge in latent domains, which could effectively guide the model towards more effective learning of the original data distribution.
• Self-supervised feature learning for cardiac Cine MR image reconstruction
  29 May 2025 1 repository listed
  We propose a self-supervised feature learning assisted reconstruction (SSFL-Recon) framework for MRI reconstruction to address the limitation of existing supervised learning methods.

Syntology lines on 10 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.