Simultaneously extracting multiple parameters via fitting one single autocorrelation function curve in diffuse correlation spectroscopy

Lixin Dong, Lian He, Yu Lin, Yu Shang, Guoqiang Yu

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Near-infrared diffuse correlation spectroscopy (DCS) has recently been employed for noninvasive acquisition of blood flow information in deep tissues. Based on the established correlation diffusion equation, the light intensity autocorrelation function detected by DCS is determined by a blood flow index αDB, tissue absorption coefficient μa, reduced scattering coefficient μ′ s and a coherence factor β. This study is designed to investigate the possibility of extracting multiple parameters such as μa, μ′ s, β, and αDB through fitting one single autocorrelation function curve and evaluate the performance of different fitting methods. For this purpose, computer simulations, tissue-like phantom experiments, and in vivo tissue measurements were utilized. The results suggest that it is impractical to simultaneously fit αDB and μa or αD B and μ′ s from one single autocorrelation function curve due to the large crosstalk between these paired parameters. However, simultaneously fitting β and αDB is feasible and generates more accurate estimation with smaller standard deviation compared to the conventional two-step fitting method (i.e., first calculating β and then fitting αDB). The outcomes from this study provide a crucial guidance for DCS data analysis.

Original languageEnglish
Article number6353906
Pages (from-to)361-368
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Volume60
Issue number2
DOIs
StatePublished - 2013

Keywords

  • Autocorrelation function
  • blood flow
  • diffuse correlation spectroscopy
  • near-infrared (NIR) spectroscopy
  • noise model

ASJC Scopus subject areas

  • Biomedical Engineering

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