ReviewDiagnostics (Basel, Switzerland)2021
Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Variable flip angle T1 mapping for quantitative liver imaging at 0.55 T.Magma (New York, N.Y.) · 2026Article
- Mechanisms of inflammatory response in secondary brain injury: a review of computational approaches, challenges, and future directions.Biomechanics and modeling in mechanobiology · 2026Review
- Cross-Site Generalization of CNN-BasedNMR in biomedicine · 2026Article
- How to implement a radiologist led whole-body MRI screening program.Radiology advances · 2026Review
- Liver fat quantification at 0.55 T enabled by locally low-rank enforced deep learning reconstruction.Magnetic resonance in medicine · 2026Article
- Practical Considerations in Abdominal MRI: Sequences, Patient Preparation, and Clinical Applications.Methods and protocols · 2025Article
- 3D gadolinium-enhanced high-resolution near-isotropic pancreatic imaging at 3.0-T MR using deep-learning reconstruction.Insights into imaging · 2025Article
- Review
- Deep learning MRI halves scan time and preserves image quality across routine neuroradiologic examinations.Radiology advances · 2025Article
- Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla.BMC medical imaging · 2025Article
- Deep learning enabled near-isotropic CAIPIRINHA VIBE in the nephrogenic phase improves image quality and renal lesion conspicuity.European journal of radiology open · 2025Article
- Deep learning reconstruction for accelerated 3-D magnetic resonance cholangiopancreatography.La Radiologia medica · 2025Article
- Fast MRI Techniques of the Liver and Pancreaticobiliary Tract: Overview and Application.Journal of the Korean Society of Radiology · 2025Review
- AI-augmented reconstruction provides improved image quality and enables shorter breath-holds in contrast-enhanced liver MRI.European radiology experimental · 2025Article
- Deep Learning-Accelerated Non-Contrast Abbreviated Liver MRI for Detecting Malignant Focal Hepatic Lesions: Dual-Center Validation.Korean journal of radiology · 2025Article
- Application of machine learning in dentistry: insights, prospects and challenges.Acta odontologica Scandinavica · 2025Review
- Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review.JNMA; journal of the Nepal Medical Association · 2025Review
- High-resolution deep learning-reconstructed T2-weighted imaging for the improvement of image quality and extraprostatic extension assessment in prostate MRI.Frontiers in radiology · 2025Article
- Enhanced bone assessment of the shoulder using zero-echo time MRI with deep-learning image reconstruction.Skeletal radiology · 2024Article
- Deep learning-accelerated T2WI of the prostate for transition zone lesion evaluation and extraprostatic extension assessment.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning technologies and applications demonstrate one of the most important upcoming developments in radiology. The impact and influence of these technologies on image acquisition and reporting might change daily clinical practice. The aim of this review was to present current deep learning technologies, with a focus on magnetic resonance image reconstruction. The first part of this manuscript concentrates on the basic technical principles that are necessary for deep learning image reconstruction. The second part highlights the translation of these techniques into clinical practice. The third part outlines the different aspects of image reconstruction techniques, and presents a review of the current literature regarding image reconstruction and image post-processing in MRI. The promising results of the most recent studies indicate that deep learning will be a major player in radiology in the upcoming years. Apart from decision and diagnosis support, the major advantages of deep learning magnetic resonance imaging reconstruction techniques are related to acquisition time reduction and the improvement of image quality. The implementation of these techniques may be the solution for the alleviation of limited scanner availability via workflow acceleration. It can be assumed that this disruptive technology will change daily routines and workflows permanently.
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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.