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20 December 2022 Real-time quantitative phase imaging through a multicore fiber using deep learning
Jiawei Sun, Nektarios Koukourakis, Jürgen W. Czarske
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Proceedings Volume 12318, Holography, Diffractive Optics, and Applications XII; 123180F (2022) https://doi.org/10.1117/12.2642514
Event: SPIE/COS Photonics Asia, 2022, Online Only
Abstract
Quantitative phase imaging (QPI) is a vital label-free measurement modality in biomedicine, providing both morphology and quantitative biophysical information. Nevertheless, applying QPI in vivo or in hard-to-reach areas remains challenging. The multicore fiber bundle (MCF) is an emerging ultra-thin probe with a diameter of less than half a millimeter. QPI through MCFs is still difficult to achieve due to the different phase delays in 10,000 fiber cores. We propose a novel speckle reconstruction method using deep learning to achieve real-time phase reconstruction through a bare MCF. High fidelity phase image reconstruction of complex image datasets like ImageNet is achieved. The network further demonstrates good generalization behavior. Phase images belonging to a different data type, such as cancer tissue and test charts, can still be reconstructed through the MCF using the network. Such a powerful technique could open new perspectives for fiber endoscopic imaging.
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Jiawei Sun, Nektarios Koukourakis, and Jürgen W. Czarske "Real-time quantitative phase imaging through a multicore fiber using deep learning", Proc. SPIE 12318, Holography, Diffractive Optics, and Applications XII, 123180F (20 December 2022); https://doi.org/10.1117/12.2642514
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KEYWORDS
Phase imaging
Speckle pattern
In vivo imaging
Cancer
Endoscopy
Image restoration
Neural networks
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