Sm-Net OCT: a deep-learning-based speckle-modulating optical coherence tomography
| Autor: | Ni Guangming, Ming Zeng, Ying Chen, Xiaoshan Wang, Yong Liu, Renxiong Wu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
genetic structures
medicine.diagnostic_test business.industry Computer science Image quality Image processing Speckle noise eye diseases Atomic and Molecular Physics and Optics Speckle pattern Optics Optical coherence tomography Temporal resolution Performed Imaging Medical imaging medicine sense organs business |
| Zdroj: | Optics express. 29(16) |
| ISSN: | 1094-4087 |
| Popis: | Speckle imposes obvious limitations on resolving capabilities of optical coherence tomography (OCT), while speckle-modulating OCT can efficiently reduce speckle arbitrarily. However, speckle-modulating OCT seriously reduces the imaging sensitivity and temporal resolution of the OCT system when reducing speckle. Here, we proposed a deep-learning-based speckle-modulating OCT, termed Sm-Net OCT, by deeply integrating conventional OCT setup and generative adversarial network trained with a customized large speckle-modulating OCT dataset containing massive speckle patterns. The customized large speckle-modulating OCT dataset was obtained from the aforementioned conventional OCT setup rebuilt into a speckle-modulating OCT and performed imaging using different scanning parameters. Experimental results demonstrated that the proposed Sm-Net OCT can effectively obtain high-quality OCT images without the electronic noise and speckle, and conquer the limitations of reducing the imaging sensitivity and temporal resolution which conventional speckle-modulating OCT has. The proposed Sm-Net OCT can significantly improve the adaptability and practicality capabilities of OCT imaging, and expand its application fields. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|