ArticleFrontiers in neuroimaging2025
Denoising very low-field magnetic resonance images using native noise modeling.
Article in Frontiers in neuroimaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Fast zero-shot deep learning-based denoising method for low-field MR images.Magma (New York, N.Y.) · 2026Article
- Deep Learning for Brain MRI Artifact Correction: Current Challenges and Future Directions.Bioengineering (Basel, Switzerland) · 2026Review
- A scoping review of portable ultra-low-field MRI studies in patients with acquired brain injury: Past, present, and future.Neuroimage. Reports · 2026Review
- Portable neuroimaging and AI: democratizing brain diagnostics.Frontiers in neurologyArticle
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Low-field MRI is gaining interest, especially in low-resource settings, due to its low cost, portability, small footprint, and low power consumption. However, it suffers from significant noise, limiting its clinical utility. This study introduces native noise denoising (NND), which leverages the inherent noise characteristics of the acquired low-field data. By obtaining the noise characteristics from corner patches of low-field images, we iteratively added similar noise to high-field images to create a paired noisy-clean dataset. A U-Net based denoising autoencoder was trained on this dataset and evaluated on three low-field datasets: the M4Raw dataset (0.3T),
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