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Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)

Author

Listed:
  • Qihang Zhang

    (Massachusetts Institute of Technology)

  • Janaka C. Gamekkanda

    (Massachusetts Institute of Technology)

  • Ajinkya Pandit

    (Massachusetts Institute of Technology)

  • Wenlong Tang

    (Takeda Pharmaceuticals International Co)

  • Charles Papageorgiou

    (Takeda Pharmaceuticals International Co)

  • Chris Mitchell

    (Takeda Pharmaceuticals International Co)

  • Yihui Yang

    (Takeda Pharmaceuticals International Co)

  • Michael Schwaerzler

    (Takeda Pharmaceutical Company Limited)

  • Tolutola Oyetunde

    (Takeda Pharmaceutical Company Limited)

  • Richard D. Braatz

    (Massachusetts Institute of Technology)

  • Allan S. Myerson

    (Massachusetts Institute of Technology)

  • George Barbastathis

    (Massachusetts Institute of Technology
    Singapore-MIT Alliance for Research and Technology (SMART) Centre)

Abstract

Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.

Suggested Citation

  • Qihang Zhang & Janaka C. Gamekkanda & Ajinkya Pandit & Wenlong Tang & Charles Papageorgiou & Chris Mitchell & Yihui Yang & Michael Schwaerzler & Tolutola Oyetunde & Richard D. Braatz & Allan S. Myerso, 2023. "Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36816-2
    DOI: 10.1038/s41467-023-36816-2
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    References listed on IDEAS

    as
    1. Dongyu Du & Xin Jin & Rujia Deng & Jinshi Kang & Hongkun Cao & Yihui Fan & Zhiheng Li & Haoqian Wang & Xiangyang Ji & Jingyan Song, 2022. "A boundary migration model for imaging within volumetric scattering media," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Y. Jauregui-Sánchez & H. Penketh & J. Bertolotti, 2022. "Author Correction: Tracking moving objects through scattering media via speckle correlations," Nature Communications, Nature, vol. 13(1), pages 1-1, December.
    3. Y. Jauregui-Sánchez & H. Penketh & J. Bertolotti, 2022. "Tracking moving objects through scattering media via speckle correlations," Nature Communications, Nature, vol. 13(1), pages 1-6, December.
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