https://scholars.lib.ntu.edu.tw/handle/123456789/636615
標題: | Synthetic high-resolution, volumetric and wide field-of-view optical coherence tomography images with generative adversarial networks | 作者: | Chueh, Chuan Bor Chen, Ting Hao Li, Yu Yu Tu, Ming Che SHIH-JUNG CHENG Lee, Cheng Kuang See, Simon Hsiang-Chieh Lee |
關鍵字: | deep learning | GAN | multi-scale imaging | optical coherence tomography | super-resolution | 公開日期: | 1-一月-2023 | 卷: | 12632 | 來源出版物: | Proceedings of SPIE - The International Society for Optical Engineering | 摘要: | Optical coherence tomography (OCT) has been widely used in many clinical apartments. The trade-off between field-of-view (FOV) and transverse resolution has always become critical. High-resolution (HR) image reconstruction of OCT has the potential to increase the resolution without reducing the FOV. Although it has been widely explored in these years, it is hard to find one-to-one paired high-resolution for reference. The SR methods are used to improve the cross-sectional OCT image only. Therefore, we build a custom-design, wide field-of-view (FOV), multi-scale optical coherence tomography that can allow us to produce excellent high- and low-resolution one-to-one mapping volumetric data set. With the SR methods, we can reduce the acquisition of synthetic high-resolution volumetric images by up to four times. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/636615 | ISBN: | 9781510664739 | ISSN: | 0277786X | DOI: | 10.1117/12.2670516 |
顯示於: | 電機工程學系 |
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