Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model
Abstract Light microscopy is a practical tool for advancing biomedical research and diagnostics, offering invaluable insights into the cellular and subcellular structures of living organisms. However, diffraction and optical imperfections actively hinder the attainment of high-quality images. In rec...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Communications Engineering |
| Online Access: | https://doi.org/10.1038/s44172-024-00331-z |
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| _version_ | 1846101144899682304 |
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| author | Rui Li Gabriel della Maggiora Vardan Andriasyan Anthony Petkidis Artsemi Yushkevich Nikita Deshpande Mikhail Kudryashev Artur Yakimovich |
| author_facet | Rui Li Gabriel della Maggiora Vardan Andriasyan Anthony Petkidis Artsemi Yushkevich Nikita Deshpande Mikhail Kudryashev Artur Yakimovich |
| author_sort | Rui Li |
| collection | DOAJ |
| description | Abstract Light microscopy is a practical tool for advancing biomedical research and diagnostics, offering invaluable insights into the cellular and subcellular structures of living organisms. However, diffraction and optical imperfections actively hinder the attainment of high-quality images. In recent years, there has been a growing interest in applying deep learning techniques to overcome these challenges in light microscopy imaging. Nonetheless, the resulting reconstructions often suffer from undesirable artefacts and hallucinations. Here, we introduce a deep learning-based approach that incorporates the fundamental physics of light propagation in microscopy into the loss function. This model employs a conditioned diffusion model in a physics-informed architecture. To mitigate the issue of limited available data, we utilise synthetic datasets for training purposes. Our results demonstrate consistent enhancements in image quality and substantial reductions in artefacts when compared to state-of-the-art methods. The presented technique is intuitively accessible and allows obtaining higher quality microscopy images for biomedical studies. |
| format | Article |
| id | doaj-art-c02ff148b03d4bedb5342e41dfcc3be5 |
| institution | Kabale University |
| issn | 2731-3395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Engineering |
| spelling | doaj-art-c02ff148b03d4bedb5342e41dfcc3be52024-12-29T12:33:36ZengNature PortfolioCommunications Engineering2731-33952024-12-013111210.1038/s44172-024-00331-zMicroscopy image reconstruction with physics-informed denoising diffusion probabilistic modelRui Li0Gabriel della Maggiora1Vardan Andriasyan2Anthony Petkidis3Artsemi Yushkevich4Nikita Deshpande5Mikhail Kudryashev6Artur Yakimovich7Center for Advanced Systems Understanding (CASUS)Center for Advanced Systems Understanding (CASUS)Department of Molecular Life Sciences, University of ZurichDepartment of Molecular Life Sciences, University of ZurichIn situ Structural Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz AssociationCenter for Advanced Systems Understanding (CASUS)In situ Structural Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz AssociationCenter for Advanced Systems Understanding (CASUS)Abstract Light microscopy is a practical tool for advancing biomedical research and diagnostics, offering invaluable insights into the cellular and subcellular structures of living organisms. However, diffraction and optical imperfections actively hinder the attainment of high-quality images. In recent years, there has been a growing interest in applying deep learning techniques to overcome these challenges in light microscopy imaging. Nonetheless, the resulting reconstructions often suffer from undesirable artefacts and hallucinations. Here, we introduce a deep learning-based approach that incorporates the fundamental physics of light propagation in microscopy into the loss function. This model employs a conditioned diffusion model in a physics-informed architecture. To mitigate the issue of limited available data, we utilise synthetic datasets for training purposes. Our results demonstrate consistent enhancements in image quality and substantial reductions in artefacts when compared to state-of-the-art methods. The presented technique is intuitively accessible and allows obtaining higher quality microscopy images for biomedical studies.https://doi.org/10.1038/s44172-024-00331-z |
| spellingShingle | Rui Li Gabriel della Maggiora Vardan Andriasyan Anthony Petkidis Artsemi Yushkevich Nikita Deshpande Mikhail Kudryashev Artur Yakimovich Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model Communications Engineering |
| title | Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model |
| title_full | Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model |
| title_fullStr | Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model |
| title_full_unstemmed | Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model |
| title_short | Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model |
| title_sort | microscopy image reconstruction with physics informed denoising diffusion probabilistic model |
| url | https://doi.org/10.1038/s44172-024-00331-z |
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