Extraction of diffuse correlation spectroscopy flow index by integration of <i>N</i>th-order linear model with Monte Carlo simulation

Abstract

Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (<i>αD_B</i> ) in tissues with small volume and large curvature. We proposed an algorithm integrating <i>N</i>th-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in tissue for the extraction of <i>αD_B</i> . The volume and geometry of the measured tissue were incorporated in the Monte Carlo simulation, which overcome the semi-infinite restrictions. The algorithm was tested using computer simulations on four tissue models with varied volumes/geometries and applied on an <i>in vivo</i> stroke model of mouse. Computer simulations shows that the high-order (<i>N</i> ≥ 5) linear algorithm was more accurate in extracting <i>αD_B</i> (errors < ±2%) from the noise-free DCS data than the semi-infinite solution (errors: -5.3% to -18.0%) for different tissue models. Although adding random noises to DCS data resulted in <i>αD_B</i> variations, the mean values of errors in extracting <i>αD_B</i> were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of <i>αD_B</i> using both linear algorithm and semi-infinite solution were fairly small (errors < ±2.0%) and did not rely on the tissue volume/geometry. The experimental results from the <i>in vivo</i> stroke mice agreed with those in simulations, demonstrating the robustness of the linear algorithm. DCS with the high-order linear algorithm shows the potential for the inter-subject comparison and longitudinal monitoring of absolute BFI in a variety of tissues/organs with different volumes/geometries.