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Adaptive processing for noise attenuation in laser speckle contrast imaging.

E Morales-Vargas1, H Peregrina-Barreto1, J C Ramirez-San-Juan1

  • 1Instituto Nacional de Astrofísica, Óptica y Electrónica, Luis Enrique Erro 1, Santa Maria Tonantzintla, 72840 Puebla, México.

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|November 4, 2021
PubMed
Summary

This study introduces an adaptive windowing method to reduce noise in Laser Speckle Contrast Imaging (LSCI), improving deep blood vessel visualization. The new approach enhances contrast-to-noise ratio for clearer microvasculature imaging.

Keywords:
Adaptive processingBlood vessels visualizationImage processingLaser speckle contrast imaging

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Area of Science:

  • Biomedical optics
  • Medical imaging
  • Microcirculation analysis

Background:

  • Laser Speckle Contrast Imaging (LSCI) is crucial for analyzing microvasculature, but noise limits its depth penetration.
  • Traditional LSCI methods use fixed-size windows, which are suboptimal for varying morphologies in microvasculature images.
  • Improving deep blood vessel visualization (>300 μm) requires effective noise reduction in contrast images.

Purpose of the Study:

  • To develop and validate a novel method for reducing noise in contrast images generated by LSCI.
  • To enhance the visualization of blood vessels, particularly at greater depths.
  • To improve the accuracy and resolution of microvasculature analysis using LSCI.

Main Methods:

  • The proposed method utilizes adaptive analysis windows that dynamically adjust size and shape per pixel.
  • This adaptive processing computes contrast representations using more representative pixels for each region.
  • The technique was tested by varying blood vessel depth, frame count, and blood flow.

Main Results:

  • Adaptive processing significantly reduced noise in contrast images, leading to better blood vessel visualization.
  • Improved Contrast to Noise Ratio (CNR) was observed, with averages of 2.62 ± 1 (in-vitro) and 5.26 ± 1.7 (in-vivo).
  • The new method achieved higher CNR compared to traditional LSCI approaches.

Conclusions:

  • The adaptive windowing (awK) method effectively reduces noise in contrast images, enhancing both temporal and spatial resolution.
  • This leads to superior blood vessel visualization compared to existing state-of-the-art methods.
  • The awK method achieves better image quality with fewer required frames.