Denoising very low-field magnetic resonance images using native noise modeling Article Swipe
Tonny Ssentamu
,
Alvin Kimbowa
,
Ronald Omoding
,
Edgar Atamba
,
Pius K Mukwaya
,
George W Jjuuko
,
Sairam Geethanath
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fnimg.2025.1501801
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fnimg.2025.1501801
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), in vivo brain MRI (0.05T), and phantom images (0.05T). The NND approach demonstrated improvements in signal-to-noise ratio (SNR) of 32.76%, 19.02%, and 8.16% across the M4Raw, in vivo and phantom datasets, respectively. Qualitative assessments, including difference maps, line intensity plots, and effective receptive fields, suggested that NND preserves structural details and edges compared to random noise denoising (RND), indicating potential enhancements in visual quality. This substantial improvement in low-field imaging quality addresses the fundamental challenge of diagnostic confidence in resource-constrained settings. By mitigating the primary technical limitation of these systems, our approach expands the clinical utility of low-field MRI scanners, potentially facilitating broader access to diagnostic imaging across resource-limited healthcare environments globally.
Related Topics
Concepts
Noise (video)
Computer science
Noise reduction
Imaging phantom
Artificial intelligence
Field (mathematics)
Signal-to-noise ratio (imaging)
Software portability
Image quality
Computer vision
Pattern recognition (psychology)
Mathematics
Image (mathematics)
Physics
Optics
Telecommunications
Programming language
Pure mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnimg.2025.1501801
- https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2025.1501801/pdf
- OA Status
- diamond
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410133259
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4410133259Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnimg.2025.1501801Digital Object Identifier
- Title
-
Denoising very low-field magnetic resonance images using native noise modelingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-06Full publication date if available
- Authors
-
Tonny Ssentamu, Alvin Kimbowa, Ronald Omoding, Edgar Atamba, Pius K Mukwaya, George W Jjuuko, Sairam GeethanathList of authors in order
- Landing page
-
https://doi.org/10.3389/fnimg.2025.1501801Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2025.1501801/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2025.1501801/pdfDirect OA link when available
- Concepts
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Noise (video), Computer science, Noise reduction, Imaging phantom, Artificial intelligence, Field (mathematics), Signal-to-noise ratio (imaging), Software portability, Image quality, Computer vision, Pattern recognition (psychology), Mathematics, Image (mathematics), Physics, Optics, Telecommunications, Programming language, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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