Advanced Quantitative Phase Microscopy Achieved with Spatial Multiplexing and a Metasurface

Quantitative optical phase information provides an alternative method to observe biomedical properties, where conventional phase imaging fails. Phase retrieval typically requires multiple intensity measurements and iterative computations to ensure uniqueness and robustness against detection noise. To increase the measurement speed, we propose a single-shot quantitative phase imaging method with metasurface optics that can be conveniently integrated into conventional imaging systems with minimal modification. The improvement of the measurement speed is simultaneously made possible by combining deep learning with the transport-of-intensity equation. As a proof-of-concept, we demonstrate phase retrieval on both calibrated phase objects and biological specimens by using an imaging system integrated with our metasurface. When combined with the matched neural network, the system yields result with errors as low as 5% and increased space-bandwidth-product. A multitude of commercial applications can benefit from the compactness and rapid implementation of our proposed method.

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Publication Details

Journal
Nano Letters
Published
2025-01-22
DOI
https://doi.org/10.1021/acs.nanolett.4c06039
Citations
6
Primary Topic
Near-Field Optical Microscopy
Type
article
Field-Weighted Citation Impact
4.04

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article

Advanced Quantitative Phase Microscopy Achieved with Spatial Multiplexing and a Metasurface

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6 citations
Nano Letters
Near-Field Optical Microscopy
4.04
article

Advanced Quantitative Phase Microscopy Achieved with Spatial Multiplexing and a Metasurface

Ang Li, Guanghao Chen, Fanglin Tian, Din Ping Tsai, Zachary Burns, Junxiao Zhou, Xinyu Chen, Ming Lei, Zhaowei Liu, Jie Hu, Yu-Hwa Lo
article en
6 citations

Abstract

Quantitative optical phase information provides an alternative method to observe biomedical properties, where conventional phase imaging fails. Phase retrieval typically requires multiple intensity measurements and iterative computations to ensure uniqueness and robustness against detection noise. To increase the measurement speed, we propose a single-shot quantitative phase imaging method with metasurface optics that can be conveniently integrated into conventional imaging systems with minimal modification. The improvement of the measurement speed is simultaneously made possible by combining deep learning with the transport-of-intensity equation. As a proof-of-concept, we demonstrate phase retrieval on both calibrated phase objects and biological specimens by using an imaging system integrated with our metasurface. When combined with the matched neural network, the system yields result with errors as low as 5% and increased space-bandwidth-product. A multitude of commercial applications can benefit from the compactness and rapid implementation of our proposed method.

Nano LettersVol. 25(5)
City University of Hong Kong (HK), University of California San Diego (US), Université Bourgogne Franche-Comté (FR)
Gordon and Betty Moore Foundation
Climate action
Openalex Percentile: Top 7%
Near-Field Optical Microscopy
4.04
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