ReviewMagma (New York, N.Y.)2024
Deep learning for accelerated and robust MRI reconstruction.
Review in Magma (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed.
- Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions.European radiology experimental · 2026Trial
- Accelerating Stroke MRI With Diffusion Probabilistic Models Through Large-Scale Pre-Training and Target-Specific Fine-Tuning.Magnetic resonance in medicine · 2026Article
- MRpro: Open Framework for Model-Based, Learned, and Quantitative MR Imaging-Application to Low-Field MRI.NMR in biomedicine · 2026Article
- 3D and 4D Free-Breathing Abdominal T1-Weighted MRI in Clinical Practice Using Deep Learning Auto-Navigation and Reconstruction.Magnetic resonance in medicine · 2026Article
- Deep learning reconstruction enables accelerated T2-weighted MRI for rectal cancer staging: a prospective study of diagnostic consistency across NEX value reduction.Abdominal radiology (New York) · 2026Article
- Time-Conditioned Zero-Shot Self-Supervised Reconstruction for Accelerated 3D Ultra-Low-Field MRI.Magnetic resonance in medicine · 2026Article
- Evaluation of a novel flexible, lightweight blanket-like torso coil in pediatric MRI at 1.5-T - initial experience.Pediatric radiology · 2026Article
- Three-dimensional MRCP technique using a multi-planar voxel sequence combined with a precontrast pulse: improved image quality and robustness to respiratory motion.Abdominal radiology (New York) · 2026Article
- On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction.Magnetic resonance in medicine · 2026Article
- Deep-Learning-Based Image Reconstruction to Improve End-Diastolic and Systolic Cardiac T1 Mapping.Magnetic resonance in medicine · 2026Article
- Fast zero-shot deep learning-based denoising method for low-field MR images.Magma (New York, N.Y.) · 2026Article
- Automated generation of impressions in abdominal radiology reports using an artificial intelligence-based tool: performance compared to manual impressions.Abdominal radiology (New York) · 2026Article
- The effect of deep learning-based compressed sensing on the image quality of contrast-enhanced 3D T1-weighted images of the maxillofacial region.Oral radiology · 2026Article
- Feasibility of deep learning-accelerated ultrafast T1-weighted VIBE Dixon imaging of the pelvis for screening of metastases in prostate MRI.European radiology experimental · 2026Article
- Accelerating MRI With Longitudinally-Informed Latent Posterior Sampling.Magnetic resonance in medicine · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Domain-Conditioned and Temporal-Guided Diffusion Modeling for Accelerated Dynamic MRI Reconstruction.NMR in biomedicine · 2026Article
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- DREAMER: Rapid and Simultaneous Multiple Contrast Magnetic Resonance Imaging of Solid and Soft Tissue.Magnetic resonance in medicine · 2026Article
- Short-TR Acquisition Time-of-flight MR Angiography with Deep Learning Reconstruction: Technical Feasibility and Initial Clinical Evaluation in Moyamoya Disease.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.
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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.