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

Haonan Lin; Chien-Sheng Liao; Pu Wang; Kai-Chih Huang; Charles A. Bouman; Nan Kong; Ji-Xin Cheng

doi:10.1117/12.2256936

21 February 2017 Sparsely-sampled hyperspectral stimulated Raman scattering microscopy: a theoretical investigation

Haonan Lin, Chien-Sheng Liao, Pu Wang, Kai-Chih Huang, Charles A. Bouman, Nan Kong, Ji-Xin Cheng

Proceedings Volume 10069, Multiphoton Microscopy in the Biomedical Sciences XVII; 1006912 (2017) https://doi.org/10.1117/12.2256936
Event: SPIE BiOS, 2017, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Haonan Lin, Chien-Sheng Liao, Pu Wang, Kai-Chih Huang, Charles A. Bouman, Nan Kong, and Ji-Xin Cheng "Sparsely-sampled hyperspectral stimulated Raman scattering microscopy: a theoretical investigation", Proc. SPIE 10069, Multiphoton Microscopy in the Biomedical Sciences XVII, 1006912 (21 February 2017); https://doi.org/10.1117/12.2256936

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