3D fluorescence imaging with diffuser endoscopy and physics-informed deep learning

Tom Glosemeyer; Julian Lich; Robert Kuschmierz; Jürgen Czarske

doi:10.1117/12.2692831

13 March 2024 3D fluorescence imaging with diffuser endoscopy and physics-informed deep learning

Tom Glosemeyer, Julian Lich, Robert Kuschmierz, Jürgen Czarske

Proceedings Volume PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; PC1285705 (2024) https://doi.org/10.1117/12.2692831
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Miniaturized endoscopes offer a high potential for biomedical imaging applications. However, conventional fiberoptic endoscopes require lens systems which are not suitable for real-time 3D imaging. Instead, a diffuser is utilized for passively encoding incoherent 3D objects into 2D speckle patterns. For computational image reconstruction beyond the optical memory effect in a spatially varying system, a physics-informed neural network is employed. For the first time worldwide, we demonstrate single-shot 3D incoherent fiber imaging with keyhole access at video rate. The diffuser fiber endoscope can be applied for fluorescence imaging which is promising for in vivo deep brain diagnostics with cellular resolution.

Conference Presentation

Citation Download Citation

Tom Glosemeyer, Julian Lich, Robert Kuschmierz, and Jürgen Czarske "3D fluorescence imaging with diffuser endoscopy and physics-informed deep learning", Proc. SPIE PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, PC1285705 (13 March 2024); https://doi.org/10.1117/12.2692831

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