ArticleThe journal of machine learning for biomedical imaging2022
A review and experimental evaluation of deep learning methods for MRI reconstruction.
Article in The journal of machine learning for biomedical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Multi-contrast low-field MRI acceleration with k-space progressive learning and image-space hybrid attention fusion.Medical image analysis · 2026Article
- Enhanced muscle MRI using deep learning: shorter acquisition time with improved image quality.PeerJ · 2026Article
- Self-supervised learning for MRI reconstruction: a review and new perspective.Magma (New York, N.Y.) · 2025Review
- Robust multi-coil MRI reconstruction via self-supervised denoising.Magnetic resonance in medicine · 2025Article
- Inverse design of periodic cavities in anechoic coatings with gradient changes of radii and distances via a conditional generative adversarial network.Scientific reports · 2025Article
- Dynamic Magnetic Resonance Imaging of Whole-Stomach Motility in Rats.NMR in biomedicine · 2025Article
- Personalized and genetically engineered animal models for next-generation surgical implant validation.Annals of medicine and surgery (2012) · 2025Review
- Correlation of objective image quality metrics with radiologists' diagnostic confidence depends on the clinical task performed.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising.IEEE transactions on medical imaging · 2025Article
- Accelerating brain T2-weighted imaging using artificial intelligence-assisted compressed sensing combined with deep learning-based reconstruction: a feasibility study at 5.0T MRI.BMC medical imaging · 2025Article
- Rosette Trajectory MRI Reconstruction with Vision Transformers.Tomography (Ann Arbor, Mich.) · 2025Article
- Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.Magnetic resonance imaging · 2025Article
- Nested CNN architecture for three-dimensional dose distribution prediction in tomotherapy for prostate cancer.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.Journal of magnetic resonance imaging : JMRI · 2025Review
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Machine Learning for the Design and the Simulation of Radiofrequency Magnetic Resonance Coils: Literature Review, Challenges, and Perspectives.Sensors (Basel, Switzerland) · 2024Review
- Denoising diffusion MRI: Considerations and implications for analysis.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Bayesian reconstruction of magnetic resonance images using Gaussian processes.Scientific reports · 2023Article
- AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language.Frontiers in neuroimaging · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI. Given the rapidly growing nature of the field, it is imperative to consolidate and summarize the large number of deep learning methods that have been reported in the literature, to obtain a better understanding of the field in general. This article provides an overview of the recent developments in neural-network based approaches that have been proposed specifically for improving parallel imaging. A general background and introduction to parallel MRI is also given from a classical view of k-space based reconstruction methods. Image domain based techniques that introduce improved regularizers are covered along with k-space based methods which focus on better interpolation strategies using neural networks. While the field is rapidly evolving with plenty of papers published each year, in this review, we attempt to cover broad categories of methods that have shown good performance on publicly available data sets. Limitations and open problems are also discussed and recent efforts for producing open data sets and benchmarks for the community are examined.
Indexed as
Identifiers
35722657PMC9202830What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.