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Blood flow estimation via numerical integration of temporal autocorrelation function in diffuse correlation

Myeongsu Seong1, Yoonho Oh2, Kijoon Lee3

  • 1School of Information Science and Technology, Nantong University, Nantong, Jiangsu, China; Research Center for Intelligent Information Technology, Nantong University, Nantong, Jiangsu, China; Nantong Research Institute for Advanced Communication Technologies, Nantong, Jiangsu, China.

Computer Methods and Programs in Biomedicine
|June 21, 2022
PubMed
Summary

New methods for diffuse correlation spectroscopy (DCS) simplify blood flow monitoring. The inverse of numerical integration of squared g1 (INISg1) with thresholding offers a faster, more efficient alternative to conventional techniques.

Keywords:
Blood flowDiffuse correlation spectroscopyNumerical integration

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Area of Science:

  • Biomedical Optics
  • Optical Instrumentation
  • Physiological Monitoring

Background:

  • Diffuse correlation spectroscopy (DCS) is a key optical technique for monitoring blood flow.
  • Current signal processing methods for DCS aim to minimize system size and processing time.
  • Developing efficient algorithms is crucial for advancing DCS applications.

Purpose of the Study:

  • To propose and validate alternative signal processing methods for DCS based on numerical integration of temporal autocorrelation curves.
  • To investigate the performance of these methods in terms of accuracy and computational speed.
  • To explore the potential for miniaturizing DCS systems using microcontrollers.

Main Methods:

  • Developed two novel methods: inverse of K² (IK2) and inverse of numerical integration of squared g1 (INISg1).
  • Introduced g1 thresholding to enhance computational efficiency.
  • Validated methods using simulations, liquid phantoms, and in vivo studies.
  • Implemented and tested methods on Arduino microcontrollers (Due, Nano 33 BLE Sense, Portenta H7).

Main Results:

  • Both IK2 and INISg1 accurately captured blood flow changes in simulations and experiments.
  • INISg1 with g1 thresholding demonstrated superior performance compared to IK2.
  • INISg1 with g1 thresholding on a PC and Portenta H7 was faster than state-of-the-art deep learning methods.

Conclusions:

  • INISg1 with g1 thresholding is a viable alternative for deriving relative blood flow information using DCS.
  • This approach contributes to simplifying DCS methodologies.
  • The findings support the potential for developing miniaturized, microcontroller-based DCS systems.