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Probing layered structures by multi-color backscattering polarimetry and machine learning.

Yuanhuan Zhu1, Yang Dong1, Yue Yao1

  • 1Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China.

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|August 30, 2021
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Summary
This summary is machine-generated.

This study introduces multi-color backscattering polarimetry and machine learning to non-invasively analyze layered biological tissues. This approach reveals microstructural details in skin, advancing biomedical imaging capabilities.

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Area of Science:

  • Biomedical Optics
  • Biophysics
  • Medical Imaging

Background:

  • Biological tissues often exhibit layered microstructures.
  • Non-invasive probing of these structures is crucial for diagnostics.
  • Polarization imaging offers quantitative microstructural insights.

Purpose of the Study:

  • To investigate the potential of multi-color backscattering polarimetry for analyzing layered biological tissues.
  • To develop and apply machine learning techniques to identify polarization parameters sensitive to layered structures.
  • To validate findings using simulations and experimental models.

Main Methods:

  • Acquisition of multi-color backscattering Mueller matrix images from living nude mice skin.
  • Experimental generation of Mueller matrices for layered tissue phantoms (e.g., silk in milk).
  • Application of supervised machine learning to identify sensitive polarization parameters.
  • Monte Carlo simulations to correlate parameters with microstructural properties.

Main Results:

  • Preliminary analysis of anisotropy (A) and linear polarizance (b) parameters indicated layered structures in skin.
  • Machine learning identified key polarization parameters sensitive to layered sample characteristics.
  • Simulations confirmed the relationship between polarization parameters and underlying microstructures.

Conclusions:

  • Multi-color backscattering polarimetry is effective for probing microstructures in layered biological tissues.
  • Supervised machine learning enhances the analysis of polarization data for structural identification.
  • This combined approach shows promise for non-invasive biomedical tissue characterization.