Extraction of diffuse correlation spectroscopy flow index by integration of Nth-order linear model with Monte Carlo simulation

Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (alpha D-B) in tissues with small volume and large curvature. We proposed an algorithm integrating Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in tissue for the extraction of alpha D-B. 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 in vivo stroke model of mouse. Computer simulations shows that the high-order (N >= 5) linear algorithm was more accurate in extracting alpha D-B (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 alpha D-B variations, the mean values of errors in extracting alpha D-B were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of alpha D-B 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 in vivo 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. (c) 2014 AIP Publishing LLC.