ArticleScientific reports2022
Deep learning for fast low-field MRI acquisitions.
Article in Scientific reports, 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
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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.
- Deep Learning Reconstruction Enables Diagnostic-Quality 0.4T Knee and Spine MRI in One-Third of the Time.NMR in biomedicine · 2026Article
- Magnetic resonance urography (MRU): technique mastery and illustrative case reviews.Abdominal radiology (New York) · 2026Review
- GLAPAL-H: Global, Local, and Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.IEEE transactions on bio-medical engineering · 2026Article
- In-silico comparison of a diffusion model with conventionally trained deep networks for translating 64mT to 3T brain FLAIR.Scientific reports · 2025Article
- GLAPAL-H: Global, Local, And Parts Aware Learner for Hydrocephalus Infection Diagnosis in Low-Field MRI.medRxiv : the preprint server for health sciences · 2025Article
- Review
- Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques.Magma (New York, N.Y.) · 2025Article
- Principles of artificial intelligence in radiooncology.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.ArXiv · 2025Article
- MRI at low field: A review of software solutions for improving SNR.NMR in biomedicine · 2025Review
- Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Fast MRI Reconstruction Using Deep Learning-based Compressed Sensing: A Systematic Review.ArXiv · 2024Article
- Environmental Sustainability and MRI: Challenges, Opportunities, and a Call for Action.Journal of magnetic resonance imaging : JMRI · 2024Review
- Encoding scheme design for gradient-free, nonlinear projection imaging using Bloch-Siegert RF spatial encoding in a low-field, open MRI system.Scientific reports · 2024Article
- Review
- New clinical opportunities of low-field MRI: heart, lung, body, and musculoskeletal.Magma (New York, N.Y.) · 2024Review
- Super resolution using sparse sampling at portable ultra-low field MR.Frontiers in neurology · 2024Article
- Low-field MRI: A report on the 2022 ISMRM workshop.Magnetic resonance in medicine · 2023Review
- Deep learning enabled fast 3D brain MRI at 0.055 tesla.Science advances · 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
4 authors.
Funding
Abstract
Low-field (LF) MRI research currently gains momentum from its potential to offer reduced costs and reduced footprints translating into wider accessibility. However, the impeded signal-to-noise ratio inherent to lower magnetic fields can have a significant impact on acquisition times that challenges LF clinical relevance. Undersampling is an effective way to speed up acquisitions in MRI, and recent work has shown encouraging results when combined with deep learning (DL). Yet, training DL models generally requires large databases that are not yet available at LF regimes. Here, we demonstrate the capability of Residual U-net combined with data augmentation to reconstruct magnitude and phase information of undersampled LF MRI scans at 0.1 T with a limited training dataset (n = 10). The model performance was first evaluated in a retrospective study for different acceleration rates and sampling patterns. Ultimately, the DL approach was validated on prospectively acquired, fivefold undersampled LF data. With varying performances associated to the adopted sampling scheme, our results show that the approach investigated can preserve the global structure and the details sharpness in the reconstructed magnitude and phase images. Overall, promising results could be obtained on acquired LF MR images that may bring this research closer to clinical implementation.
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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.