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Detection of Protein Aggregation using Fluorescence Correlation Spectroscopy
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Published on: April 25, 2021

Diffuse correlation spectroscopy with a fast Fourier transform-based software autocorrelator.

Jing Dong1, Renzhe Bi, Jun Hui Ho

  • 1Nanyang Technological University, Division of Bioengineering, School of Chemical and Biomedical Engineering, Singapore 637457, Singapore.

Journal of Biomedical Optics
|October 23, 2012
PubMed
Summary
This summary is machine-generated.

A new software-based autocorrelator for diffuse correlation spectroscopy (DCS) offers a cost-effective and flexible alternative to hardware systems for measuring deep tissue blood flow.

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Area of Science:

  • Biomedical Optics
  • Medical Instrumentation
  • Optical Spectroscopy

Background:

  • Diffuse correlation spectroscopy (DCS) is a noninvasive optical technique for assessing deep tissue blood flow.
  • Conventional DCS systems often rely on hardware correlators for data processing.
  • There is a need for more flexible and cost-effective DCS data processing solutions.

Purpose of the Study:

  • To develop and validate a fast Fourier transform (FFT)-based software autocorrelator for DCS.
  • To evaluate the performance of the software autocorrelator compared to traditional hardware correlators.
  • To demonstrate the utility of the software autocorrelator in various experimental settings.

Main Methods:

  • Development of a LabVIEW-based software autocorrelator utilizing FFT for DCS data acquisition and processing.
  • Experimental validation using an in-house flow phantom, human forearm, and mouse tumors.
  • Acquisition rates of approximately 400 kHz were employed.
  • Comparison of software and hardware correlator performance, focusing on data processing, fitting, and blood flow index (BFI) linearity.

Main Results:

  • The FFT-based software autocorrelator demonstrated flexibility in data preprocessing and lower cost.
  • Smoother starting and ending plateaus were observed in the software autocorrelator compared to hardware correlators.
  • The software autocorrelator achieved comparable speeds to hardware correlators.
  • Blood flow index (BFI) derived from the software autocorrelator showed improved linear behavior in phantom experiments.

Conclusions:

  • An FFT-based software autocorrelator presents a viable and beneficial alternative to conventional hardware correlators in DCS systems.
  • This software approach offers advantages in cost, flexibility, and data processing quality.
  • The developed system shows promise for advancing noninvasive blood flow monitoring applications.