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Sparse Photoacoustic Microscopy Reconstruction Based on Matrix Nuclear Norm Minimization

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Machine Learning and Intelligent Communications (MLICOM 2017)

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Abstract

As a high-resolution deep tissue imaging technology, photoacoustic microscopy (PAM) is attracting extensive attention in biomedical studies. PAM has trouble in achieving real-time imaging with the long data acquisition time caused by point-to-point sample mode. In this paper, we propose a sparse photoacoustic microscopy reconstruction method based on matrix nuclear norm minimization. We use random sparse sampling instead of traditional full sampling and regard the sparse PAM reconstruction problem as a nuclear norm minimization problem, which is efficiently solved under alternating direction method of multiplier (ADMM) framework. Results from PAM experiments indicate the proposed method could work well in fast imaging. The proposed method is also be expected to promote the achievement of PAM real-time imaging.

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Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (Grant No. 61371045), Science and Technology Development Plan Project of Shandong Province, China (Grant No. 2015GGX103016, 2016GGX103032) and the China Postdoctoral Science Foundation (Grant No. 2015M571413).

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Authors and Affiliations

  1. Department of Control Science and Engineering, Harbin Institute of Technology at Weihai, Weihai, 264209, China

    Ying Fu, Naizhang Feng, Yahui Shi & Mingjian Sun

  2. Department of Measurement and Control Technology and Instrumentation, Dalian Maritime University, Dalian, 116026, China

    Ting Liu

Authors
  1. Ying Fu
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  2. Naizhang Feng
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  3. Yahui Shi
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  4. Ting Liu
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  5. Mingjian Sun
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Corresponding author

Correspondence to Mingjian Sun .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Fu, Y., Feng, N., Shi, Y., Liu, T., Sun, M. (2018). Sparse Photoacoustic Microscopy Reconstruction Based on Matrix Nuclear Norm Minimization. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_6

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  • DOI: https://doi.org/10.1007/978-3-319-73564-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73563-4

  • Online ISBN: 978-3-319-73564-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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