Simultaneously Extracting Multiple Parameters via Fitting One Single Autocorrelation Function Curve in Diffuse Correlation Spectroscopy

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 μs^´, and a coherence factor β. This study is designed to investigate the possibility of extracting multiple parameters such as μa, μs^´, 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 αDB and μs^´ 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.