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Random-walk scanning microscopy for fast molecular image acquisition (Conference Presentation)
Author(s): Satoshi Kawata

Paper Abstract

Compared to the conventional uniform illumination microscopes, a laser scanning microscope takes much longer time of image acquisition, because a focus-beam scans a sample pixel by pixel in 2D or even in 3D. In this presentation, I will discuss a new scanning method that extremely reduces the time of imaging in scanning microscopy. The method is beneficial to the samples that weakly scattering with sparsely distributed molecules or nano-materials. Raman scattering microscopy, dark-field microscopy, and phase-contrast microscopy are good examples. The proposed method of scanning is based on statistics and stochastic process theory. The method mimics the human's behavior of finding unknown places in a large map and animal’s hunting their prey from a large area. In the method, starting points of searching are given by a random distribution (the first layer), and the scanning starts at individual starting points to diffuse the search area based on a given stochastic process (the second layer). The diffusion area of scanning is limited by the entropy of local information of the sample. We will show experimental results of Raman scattering microscopic images obtained with the proposed scanning method and will compare the results with those obtained by conventional deterministic scanning paths. The effectiveness and the limit of the method will be also discussed. The idea proposed here is an extension of our previous work for intracellular nano-Raman microscope with a plasmonic nanoparticle [1]. [1] J. Ando.et. al., Nano Lett. 11, 5344, 2011

Paper Details

Date Published: 10 September 2019
PDF
Proc. SPIE 11098, Molecular and Nano Machines II, 1109803 (10 September 2019); doi: 10.1117/12.2530074
Show Author Affiliations
Satoshi Kawata, Osaka Univ. (Japan)
Nanophoton Corp. (Japan)


Published in SPIE Proceedings Vol. 11098:
Molecular and Nano Machines II
Zouheir Sekkat; Takashige Omatsu, Editor(s)

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