Current Optics and Photonics

Optical Society of Korea (한국광학회)
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Fast Noise Reduction Approach in Multifocal Multiphoton Microscopy Based on Monte-Carlo Simulation

  • Kim, Dongmok (Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology) ;
  • Shin, Younghoon (Medical & Bio Photonics Research Center, Korea Photonics Technology Institute) ;
  • Kwon, Hyuk-Sang (Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology)
  • Received : 2021.04.13
  • Accepted : 2021.05.27
  • Published : 2021.08.25
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Abstract

The multifocal multiphoton microscopy (MMM) enables high-speed imaging by the concurrent scanning and detection of multiple foci generated by lenslet array or diffractive optical element. The MMM system mainly suffers from crosstalk generated by scattered emission photons that form ghost images among adjacent channels. The ghost image which is a duplicate of the image acquired in sub-images significantly degrades overall image quality. To eliminate the ghost image, the photon reassignment method was established using maximum likelihood estimation. However, this post-processing method generally takes a longer time than image acquisition. In this regard, we propose a novel strategy for rapid noise reduction in the MMM system based upon Monte-Carlo (MC) simulation. Ballistic signal, scattering signal, and scattering noise of each channel are quantified in terms of photon distribution launched in tissue model based on MC simulation. From the analysis of photon distribution, we successfully eliminated the ghost images in the MMM sub-images. If the priori MC simulation under a certain optical condition is established at once, our simple, but robust post-processing technique will continuously provide the noise-reduced images, while significantly reducing the computational cost.

Keywords

Acknowledgement

This study was supported by the National Research foundation of Korea grant funded by the Korea government (2019R1A2C2090661, NRF-2020M3C7A1021731), GIST Research Institute (GRI) grant funded by the GIST (1711122918).

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