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All-Dielectric Meta-Microlens-Array Confocal Fluorescence Microscopy

  • Surag Athippillil Suresh (Co-first Author)
  • , Sunil Vyas (Co-first Author)
  • , Cheng Hung Chu
  • , Takeshi Yamaguchi
  • , Takuo Tanaka
  • , J. Andrew Yeh*
  • , Din Ping Tsai*
  • , Yuan Luo*
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Acquisition time and optical sectioning capability are critical factors in fluorescence imaging. Confocal microscopy is a vital optical imaging method to inherently observe volumetric tissues with fine optical sectioning capability; however, point-by-point scanning is time-consuming. Metasurfaces, a type of flat optics utilizing nano-scale structures, provide diverse functionalities and extensive flexibility in controlling light wavefronts. Here, meta-microlens-array (meta-MLA) for multifocal confocal fluorescence microscopy to enhance acquisition speed is introduced, reduce photo-bleaching, and improve energy efficiency while remaining compatible with existing commercial scanning configurations. Point spread function (PSF) in the meta-MLA confocal lateral and axial directions has been evaluated. Fast optically sectioned images of various samples, including pollen grains and biological tissue phantoms, are performed. Image quality is further enhanced by the Richardson–Lucy (RL) deconvolution method with total variation (TV). The trade-off between spatial resolution and acquisition speed is overcome using deep neural network models, comparing performance metrics with a conventional confocal microscope. The combination of meta-MLA confocal and deep learning with superior image quality and fast acquisition will likely extend the clinical applications of miniaturized optical imaging. © 2024 Wiley-VCH GmbH.
Original languageEnglish
Article number2401314
JournalLaser and Photonics Reviews
Volume19
Issue number6
Online published24 Dec 2024
DOIs
Publication statusPublished - 18 Mar 2025

Funding

S.A.S. and S.V. contributed equally to this work. S.A.S., S.V., and C.H.C. conceived and performed the numerical design, optical measurement, and data analysis. S.A.S., S.V., J.A.Y., and Y.L. co-wrote the manuscript. C.H.C., T.Y., and T.T. performed metasurfaces preparation. S.A.S. built up the optical system for measurement and performed imaging experiments. J.A.Y., D.P.T, and Y.L. organized the project, designed experiments, analyzed the results, and prepared the manuscript. All authors discussed the results and commented on the manuscript. The authors acknowledge the financial support from the National Science and Technology Council, Taiwan, R.O.C. (Grant Nos. NSTC 112-2221-E-002 -055 -MY3, NSTC 112-2221-E-002 -212 -MY3, NSTC 113-2221-E-007-061-MY3 and NSTC 110-2221-E-007-068-MY3), National Taiwan University, Taiwan, R.O.C. (Grant No. NTU-CC-112L892902, NTU-113L8507, and NTU-CC-113L891102), National Health Research Institutes, Taiwan, R.O.C. (Grant No. NHRI-EX113-11327EI), Fitipower Integrated Technology Inc, Taiwan, R.O.C. (Grant No. 113H1010-C01), the University Grants Committee/Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. AoE/P-502/20, CRF Project: C1015-21E; C5031-22G, GRF Project: CityU15303521; CityU11305223; CityU11300224, and Germany/Hong Kong Joint Research Scheme: G-CityU 101/22), City University of Hong Kong (Project No. 9380131 and 7005867), and JST CREST (Grant No. JPMJCR1904).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Research Keywords

  • confocal fluorescence microscopy
  • deconvolution
  • deep learning
  • meta-microlens-array

RGC Funding Information

  • RGC-funded

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