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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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Comparison of numerical-integration-based methods for blood flow estimation in diffuse correlation spectroscopy.

Myeongsu Seong1

  • 1Research Center for Intelligent Information Technology, Nantong University, Nantong 226019, China; Department of Mechatronics and Robotics, School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.

Computer Methods and Programs in Biomedicine
|August 30, 2023
PubMed
Summary
This summary is machine-generated.

The original INISg1 method for diffuse correlation spectroscopy (DCS) blood flow monitoring is robust in most conditions. Variants showed improved performance in extreme scenarios, guiding researchers in selecting appropriate signal processing techniques.

Keywords:
Bio-signal processingBlood flowDiffuse correlation spectroscopyDiffuse opticsNumerical integration

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Area of Science:

  • Biomedical Optics
  • Optical Sensing
  • Blood Flow Monitoring

Background:

  • Diffuse Correlation Spectroscopy (DCS) is a key technology for optical blood flow monitoring.
  • Signal processing in DCS, particularly nonlinear fitting, can limit real-time applications.
  • The INISg1 method was previously introduced to accelerate DCS signal processing.

Purpose of the Study:

  • To introduce and compare variants of the INISg1 method for DCS signal processing.
  • To evaluate the robustness and speed of INISg1 and its variants under various conditions.
  • To provide guidance for researchers implementing INISg1 in diverse DCS applications.

Main Methods:

  • Developed INISg1 variants using the right Riemann sum (INISg1_RR) and trapezoid rule (INISg1_TR) for numerical integration.
  • Conducted model-based simulations to control experimental parameters like integration time, β, and photon count rate.
  • Compared the performance and processing speed of original INISg1 against its variants.

Main Results:

  • The original INISg1 demonstrated robust performance across most simulated conditions.
  • INISg1 variants (INISg1_RR, INISg1_TR) exhibited superior performance in extreme experimental conditions.
  • INISg1 achieved signal processing speeds 1.63x and 1.98x faster than INISg1_RR and INISg1_TR, respectively.

Conclusions:

  • INISg1 is a reliable method for DCS signal processing in typical scenarios.
  • The study provides valuable insights for selecting appropriate INISg1 variants based on experimental conditions.
  • This research serves as a guide for optimizing real-time blood flow monitoring using DCS.