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Proceedings Paper

Sparsely-sampled hyperspectral stimulated Raman scattering microscopy: a theoretical investigation
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Paper Abstract

A hyperspectral image corresponds to a data cube with two spatial dimensions and one spectral dimension. Through linear un-mixing, hyperspectral images can be decomposed into spectral signatures of pure components as well as their concentration maps. Due to this distinct advantage on component identification, hyperspectral imaging becomes a rapidly emerging platform for engineering better medicine and expediting scientific discovery. Among various hyperspectral imaging techniques, hyperspectral stimulated Raman scattering (HSRS) microscopy acquires data in a pixel-by-pixel scanning manner. Nevertheless, current image acquisition speed for HSRS is insufficient to capture the dynamics of freely moving subjects. Instead of reducing the pixel dwell time to achieve speed-up, which would inevitably decrease signal-to-noise ratio (SNR), we propose to reduce the total number of sampled pixels. Location of sampled pixels are carefully engineered with triangular wave Lissajous trajectory. Followed by a model-based image in-painting algorithm, the complete data is recovered for linear unmixing. Simulation results show that by careful selection of trajectory, a fill rate as low as 10% is sufficient to generate accurate linear unmixing results. The proposed framework applies to any hyperspectral beam-scanning imaging platform which demands high acquisition speed.

Paper Details

Date Published: 21 February 2017
PDF: 12 pages
Proc. SPIE 10069, Multiphoton Microscopy in the Biomedical Sciences XVII, 1006912 (21 February 2017); doi: 10.1117/12.2256936
Show Author Affiliations
Haonan Lin, Purdue Univ. (United States)
Chien-Sheng Liao, Purdue Univ. (United States)
Pu Wang, Purdue Univ. (United States)
Kai-Chih Huang, Purdue Univ. (United States)
Charles A. Bouman, Purdue Univ. (United States)
Nan Kong, Purdue Univ. (United States)
Ji-Xin Cheng, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 10069:
Multiphoton Microscopy in the Biomedical Sciences XVII
Ammasi Periasamy; Peter T. C. So; Karsten König; Xiaoliang S. Xie, Editor(s)

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