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Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning.

Martin Hultman1,2, Marcus Larsson1, Tomas Strömberg1

  • 1Linköping University, Department of Biomedical Engineering, Linköping, Sweden.

Journal of Biomedical Optics
|March 23, 2023
PubMed
Summary

This study introduces a new method using artificial neural networks and multi-exposure laser speckle contrast imaging (MELSCI) to accurately measure blood flow speed and perfusion in absolute units. This advance improves the physiological interpretation of microcirculatory data from skin tissue.

Keywords:
artificial neural networksblood flowmicrocirculationmulti-exposure laser speckle contrast imaging

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Physiology

Background:

  • Single-exposure Laser Speckle Contrast Imaging (LSCI) provides limited information for accurate microcirculatory perfusion modeling in skin.
  • Complex optical effects like scattering and multiple Doppler shifts challenge traditional LSCI analysis.
  • Previous methods using inverse Monte Carlo (MC) algorithms in single-point Laser Doppler Flowmetry (LDF) enabled speed-resolved perfusion but not imaging.

Purpose of the Study:

  • To develop and present a novel method for speed-resolved perfusion imaging in absolute units (%RBC × mm/s).
  • To compute this perfusion measure from multi-exposure speckle contrast images using advanced algorithms.
  • To enhance the physiological interpretation of microcirculatory data obtained via imaging techniques.

Main Methods:

  • An artificial neural network (ANN) was trained using simulated multi-exposure contrast data and corresponding speed-resolved perfusion values.
  • The training dataset was generated via MC simulations of photon transport in diverse, randomized skin models.
  • The ANN model was validated using in vivo datasets acquired during occlusion provocation tests.

Main Results:

  • The ANN accurately estimated speed-resolved perfusion in three distinct speed intervals with relative errors of 9.8%, 12%, and 19%.
  • Perfusion measurements demonstrated a linear response to alterations in blood tissue fraction and flow speed.
  • The method showed reduced sensitivity to varying tissue properties compared to standard LSCI, with improved image quality revealing vascular structures.

Conclusions:

  • An ANN trained on simulated data can calculate speed-resolved perfusion in absolute units from multi-exposure speckle contrast.
  • This technique significantly enhances the physiological interpretability of MELSCI measurements.
  • The developed method holds potential to increase the clinical utility and impact of MELSCI.