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Ketabchi AM, Morova B, Uysalli Y, Aydin M, Eren F, Bavili N, Pysz D, Buczynski R, Kiraz A. Enhancing resolution and contrast in fibre bundle-based fluorescence microscopy using generative adversarial network. J Microsc 2024. [PMID: 38563195 DOI: 10.1111/jmi.13296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/04/2024]
Abstract
Fibre bundle (FB)-based endoscopes are indispensable in biology and medical science due to their minimally invasive nature. However, resolution and contrast for fluorescence imaging are limited due to characteristic features of the FBs, such as low numerical aperture (NA) and individual fibre core sizes. In this study, we improved the resolution and contrast of sample fluorescence images acquired using in-house fabricated high-NA FBs by utilising generative adversarial networks (GANs). In order to train our deep learning model, we built an FB-based multifocal structured illumination microscope (MSIM) based on a digital micromirror device (DMD) which improves the resolution and the contrast substantially compared to basic FB-based fluorescence microscopes. After network training, the GAN model, employing image-to-image translation techniques, effectively transformed wide-field images into high-resolution MSIM images without the need for any additional optical hardware. The results demonstrated that GAN-generated outputs significantly enhanced both contrast and resolution compared to the original wide-field images. These findings highlight the potential of GAN-based models trained using MSIM data to enhance resolution and contrast in wide-field imaging for fibre bundle-based fluorescence microscopy. Lay Description: Fibre bundle (FB) endoscopes are essential in biology and medicine but suffer from limited resolution and contrast for fluorescence imaging. Here we improved these limitations using high-NA FBs and generative adversarial networks (GANs). We trained a GAN model with data from an FB-based multifocal structured illumination microscope (MSIM) to enhance resolution and contrast without additional optical hardware. Results showed significant enhancement in contrast and resolution, showcasing the potential of GAN-based models for fibre bundle-based fluorescence microscopy.
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Affiliation(s)
| | - Berna Morova
- Department of Physics Engineering, Istanbul Technical University, Istanbul, Türkiye
| | - Yiğit Uysalli
- Optofil, Inc., Istanbul, Türkiye
- Department of Physics, Koç University, Istanbul, Türkiye
| | - Musa Aydin
- Department of Computer Engineering, Fatih Sultan Mehmet Vakif University, Istanbul, Türkiye
| | | | - Nima Bavili
- Department of Physics, Koç University, Istanbul, Türkiye
| | - Dariusz Pysz
- Department of Glass, Institute of Electronic Materials Technology, Warsaw, Poland
| | - Ryszard Buczynski
- Department of Glass, Institute of Electronic Materials Technology, Warsaw, Poland
- Faculty of Physics, University of Warsaw, Warsaw, Poland
| | - Alper Kiraz
- Department of Electrical and Electronics Engineering, Koç University, Istanbul, Türkiye
- Optofil, Inc., Istanbul, Türkiye
- Department of Physics, Koç University, Istanbul, Türkiye
- KUTTAM-Koç University Research Center for Translational Medicine, Istanbul, Türkiye
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