ReviewJournal of magnetic resonance imaging : JMRI2021
Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.
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 31 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
31 citing papers in PubMed.
- Utility of breath-hold half-Fourier single-shot turbo spin-echo with deep learning-based reconstruction for acquiring fat-suppressed T2-weighted images of the pancreas.European journal of radiology open · 2026Article
- How deep resolve affects the stability of texture features for different MR image acceleration methods - a phantom and a human brain study.BMC medical imaging · 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
- Comparative evaluation of four reconstruction techniques for prostate T2-weighted MRI: Sensitivity encoding, compressed sensing, deep learning, and super-resolution.European journal of radiology open · 2025Article
- Accelerated brain magnetic resonance imaging with deep learning reconstruction: a comparative study on image quality in pediatric neuroimaging.Pediatric radiology · 2025Article
- Uncovering ethical biases in publicly available fetal ultrasound datasets.NPJ digital medicine · 2025Article
- Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application.Journal of the Korean Society of Radiology · 2025Review
- Use of Artificial Intelligence on Imaging and Preoperatory Planning of the Knee Joint: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- AI-accelerated T2-weighted TSE imaging of the rectum demonstrates excellent image quality with reduced acquisition time.Abdominal radiology (New York) · 2025Article
- Improved Resolution and Image Quality of Musculoskeletal Magnetic Resonance Imaging using Deep Learning-based Denoising Reconstruction: A Prospective Clinical Study.Skeletal radiology · 2024Article
- Comparison of image quality and lesion conspicuity between conventional and deep learning reconstruction in gadoxetic acid-enhanced liver MRI.Insights into imaging · 2024Article
- Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art.European journal of nuclear medicine and molecular imaging · 2024Review
- Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck.Magma (New York, N.Y.) · 2024Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction.Cancers · 2024Article
- Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality.La Radiologia medica · 2024Article
- Ocular condition prognosis in Keratoconus patients after corneal ring implantation using artificial neural networks.Heliyon · 2023Article
- Contrast Agents of Magnetic Resonance Imaging and Future Perspective.Nanomaterials (Basel, Switzerland) · 2023Review
- Deep learning-assisted diagnosis of femoral trochlear dysplasia based on magnetic resonance imaging measurements.World journal of clinical cases · 2023Article
- Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.Journal of digital imaging · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.
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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.