Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography
Abstract Background In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essenti...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00483-1 |
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| author | Vineeta Das Furu Zhang Andrew J. Bower Joanne Li Tao Liu Nancy Aguilera Bruno Alvisio Zhuolin Liu Daniel X. Hammer Johnny Tam |
| author_facet | Vineeta Das Furu Zhang Andrew J. Bower Joanne Li Tao Liu Nancy Aguilera Bruno Alvisio Zhuolin Liu Daniel X. Hammer Johnny Tam |
| author_sort | Vineeta Das |
| collection | DOAJ |
| description | Abstract Background In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput. Methods Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study. Results We show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges. Conclusions The results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting. |
| format | Article |
| id | doaj-art-d83ae0fd9aa74ce8bc9566879e38863a |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-d83ae0fd9aa74ce8bc9566879e38863a2025-02-02T12:40:08ZengNature PortfolioCommunications Medicine2730-664X2024-04-014111010.1038/s43856-024-00483-1Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomographyVineeta Das0Furu Zhang1Andrew J. Bower2Joanne Li3Tao Liu4Nancy Aguilera5Bruno Alvisio6Zhuolin Liu7Daniel X. Hammer8Johnny Tam9National Eye Institute, National Institutes of HealthNational Eye Institute, National Institutes of HealthNational Eye Institute, National Institutes of HealthNational Eye Institute, National Institutes of HealthNational Eye Institute, National Institutes of HealthNational Eye Institute, National Institutes of HealthNational Eye Institute, National Institutes of HealthCenter for Devices and Radiological Health, U.S. Food and Drug AdministrationCenter for Devices and Radiological Health, U.S. Food and Drug AdministrationNational Eye Institute, National Institutes of HealthAbstract Background In vivo imaging of the human retina using adaptive optics optical coherence tomography (AO-OCT) has transformed medical imaging by enabling visualization of 3D retinal structures at cellular-scale resolution, including the retinal pigment epithelial (RPE) cells, which are essential for maintaining visual function. However, because noise inherent to the imaging process (e.g., speckle) makes it difficult to visualize RPE cells from a single volume acquisition, a large number of 3D volumes are typically averaged to improve contrast, substantially increasing the acquisition duration and reducing the overall imaging throughput. Methods Here, we introduce parallel discriminator generative adversarial network (P-GAN), an artificial intelligence (AI) method designed to recover speckle-obscured cellular features from a single AO-OCT volume, circumventing the need for acquiring a large number of volumes for averaging. The combination of two parallel discriminators in P-GAN provides additional feedback to the generator to more faithfully recover both local and global cellular structures. Imaging data from 8 eyes of 7 participants were used in this study. Results We show that P-GAN not only improves RPE cell contrast by 3.5-fold, but also improves the end-to-end time required to visualize RPE cells by 99-fold, thereby enabling large-scale imaging of cells in the living human eye. RPE cell spacing measured across a large set of AI recovered images from 3 participants were in agreement with expected normative ranges. Conclusions The results demonstrate the potential of AI assisted imaging in overcoming a key limitation of RPE imaging and making it more accessible in a routine clinical setting.https://doi.org/10.1038/s43856-024-00483-1 |
| spellingShingle | Vineeta Das Furu Zhang Andrew J. Bower Joanne Li Tao Liu Nancy Aguilera Bruno Alvisio Zhuolin Liu Daniel X. Hammer Johnny Tam Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography Communications Medicine |
| title | Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography |
| title_full | Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography |
| title_fullStr | Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography |
| title_full_unstemmed | Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography |
| title_short | Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography |
| title_sort | revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography |
| url | https://doi.org/10.1038/s43856-024-00483-1 |
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