ArticleMagnetic resonance in medicine2023
Uncertainty-aware physics-driven deep learning network for free-breathing liver fat and R
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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
11 citing papers in PubMed, 14 citations in OpenAlex.
- DeepRelaxo: Fast Mono-Exponential Magnitude Brain R2* Mapping With Reduced Echoes Using Self-Supervised Deep Learning.Magnetic resonance in medicine · 2026Article
- Volumetric thermometry in moving tissues using stack-of-radial MRI and an image-navigated multi-baseline proton resonance frequency shift method.Magnetic resonance in medicine · 2026Article
- Development and validation of an artificial intelligence model based on liver CSE-MRI fat maps for predicting dyslipidemia.PLOS digital health · 2026Article
- Rapid, high-resolution and distortion-freeMagnetic resonance in medicine · 2025Article
- Rapid, High-resolution and Distortion-freeArXiv · 2025Article
- Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques.Oncotarget · 2025Article
- Chemical shift encoding based double bonds quantification in triglycerides using deep image prior.Quantitative imaging in medicine and surgery · 2025Article
- Free-breathing MRI techniques for fat and RMagma (New York, N.Y.) · 2024Review
- Accelerated four-dimensional free-breathing whole-liver water-fat magnetic resonance imaging with deep dictionary learning and chemical shift modeling.Quantitative imaging in medicine and surgery · 2024Article
- Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Deep Learning-accelerated MRI in Body and Chest.Journal of computer assisted tomographyReview
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo develop a deep learning-based method for rapid liver proton-density fat fraction (PDFF) and R
methodsThis work developed an uncertainty-aware physics-driven deep learning network (UP-Net) to (1) suppress radial streaking artifacts because of undersampling after self-gating, (2) calculate accurate quantitative maps, and (3) provide pixel-wise uncertainty maps. UP-Net incorporated a phase augmentation strategy, generative adversarial network architecture, and an MRI physics loss term based on a fat-water and R
resultsCompared with images reconstructed using compressed sensing (CS), UP-Net achieved structural similarity index >0.87 and normalized root mean squared error <0.18. Compared with reference quantitative maps generated using CS and graph-cut (GC) algorithms, UP-Net achieved low mean differences (MD) for liver PDFF (-0.36%) and R
conclusionUP-Net rapidly calculates accurate liver PDFF and R
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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.