ReviewBiomedical engineering letters2024
A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies.
Review in Biomedical engineering letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed.
- MRI-guided endovascular robotics: A roadmap to MRI-native systems.APL bioengineering · 2026Article
- Impact of deep learning-based MRI reconstruction on image quality and apparent diffusion coefficient measurements in patients with intracranial tumors: a prospective intra-individual study.Quantitative imaging in medicine and surgery · 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
- Review
- Radon-Guided Wavelet-Domain Attention U-Net for Periodic Artifact Suppression in Brain MRI.Journal of imaging · 2026Article
- A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography.Scientific reports · 2026Article
- Hidden workload in pediatric radiology: a 20-year retrospective time-trend study of the increasing number of images per study.Pediatric radiology · 2026Article
- Compressed Sensing Reconstruction with Zero-Shot Self-Supervised Learning for High-Resolution MRI of Human Embryos.Tomography (Ann Arbor, Mich.) · 2025Article
- AI for image quality and patient safety in CT and MRI.European radiology experimental · 2025Review
- Deep Learning-accelerated MRI in Body and Chest.Journal of computer assisted tomographyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accelerated magnetic resonance imaging (MRI) has played an essential role in reducing data acquisition time for MRI. Acceleration can be achieved by acquiring fewer data points in k-space, which results in various artifacts in the image domain. Conventional reconstruction methods have resolved the artifacts by utilizing multi-coil information, but with limited robustness. Recently, numerous deep learning-based reconstruction methods have been developed, enabling outstanding reconstruction performances with higher acceleration. Advances in hardware and developments of specialized network architectures have produced such achievements. Besides, MRI signals contain various redundant information including multi-coil redundancy, multi-contrast redundancy, and spatiotemporal redundancy. Utilization of the redundant information combined with deep learning approaches allow not only higher acceleration, but also well-preserved details in the reconstructed images. Consequently, this review paper introduces the basic concepts of deep learning and conventional accelerated MRI reconstruction methods, followed by review of recent deep learning-based reconstruction methods that exploit various redundancies. Lastly, the paper concludes by discussing the challenges, limitations, and potential directions of future developments.
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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.