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 alpha D-B, tissue absorption coefficient mu(a), reduced scattering coefficient mu(s)', and a coherence factor beta. This study is designed to investigate the possibility of extracting multiple parameters such as mu(a), mu(s)', beta, and alpha D-B 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 alpha D-B and mu(a) or alpha D-B and mu(s)' from one single autocorrelation function curve due to the large crosstalk between these paired parameters. However, simultaneously fitting beta and alpha D-B is feasible and generates more accurate estimation with smaller standard deviation compared to the conventional two-step fitting method (i.e., first calculating beta and then fitting alpha D-B). The outcomes from this study provide a crucial guidance for DCS data analysis.