ReviewJournal of magnetic resonance imaging : JMRI2021
Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.
Review in Journal of magnetic resonance imaging : JMRI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 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.
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Who cites it
112 citing papers in PubMed, 272 citations in OpenAlex.
- Free-Breathing Arterial Spin Labeling MRI of the Kidney Using Flow-Sensitive-Alternating Inversion Recovery With Golden-Angle Radial Sampling.NMR in biomedicine · 2026Article
- Artificial intelligence-enabled pediatric radiology in low-resource settings: addressing resource constraints in the African healthcare system.Pediatric radiology · 2026Review
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- ESR Innovation in Focus: Deep learning in MR image reconstruction.European radiology · 2026Article
- MR Optimum: A web-based open-source tool for standardized signal-to-noise ratio evaluation in MRI.Computer methods and programs in biomedicine update · 2026Article
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
- Quantitative Real-Time MRI for the Assessment of Gastric Motility.Journal of magnetic resonance imaging : JMRI · 2026Article
- Proceedings from the CIHBMC proceedings · 2026Article
- Phase-constrained zero-shot self-supervised learning for BLADE liver MRI reconstruction.Magma (New York, N.Y.) · 2026Article
- Evaluating Curvature-Induced Variation in Deep Learning-Based Beamforming for Flexible Transducers in Ultrasound-Guided Radiation Therapy.Bioengineering (Basel, Switzerland) · 2026Article
- Assessment of deep learning reconstruction effects on detection and differentiation of liver metastasis from hepatic hemangioma in diffusion-weighted imaging.Japanese journal of radiology · 2026Article
- Anticipating Moral and Economic Considerations, Opportunities, and Potential Frictions for AI in Medical Imaging: Multistakeholder Cocreation Study.Journal of medical Internet research · 2026Article
- Magnetic resonance urography (MRU): technique mastery and illustrative case reviews.Abdominal radiology (New York) · 2026Review
- Deep learning-enhanced zero echo time silent brain magnetic resonance imaging in infants without sedation.Pediatric radiology · 2026Article
- Article
- Impact of AIR™ Recon DL on magnetic resonance imaging-based quantitative brain structure measurements.Psychoradiology · 2026Article
- Ultra-fast whole-brain T2-weighted imaging in 7 seconds using dual-type deep learning reconstruction with single-shot acquisition: clinical feasibility and comparison with conventional methods.Japanese journal of radiology · 2026Article
- Article
- Accelerated magnetic resonance imaging of hippocampal sclerosis in pediatric patients with deep learning-based reconstruction: comparison of image quality and diagnostic performance with conventional reconstruction.Pediatric radiology · 2026Article
- The Effect of AI on the Radiologist Workforce: A Task-Based Analysis.medRxiv : the preprint server for health sciences · 2025Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.
Indexed as
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What 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.