## Abstract

Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (αD_{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 αD_{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 αD_{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 αD _{B} variations, the mean values of errors in extracting αD _{B} were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of αD _{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.

Original language | English (US) |
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Article number | 193703 |

Journal | Applied Physics Letters |

Volume | 104 |

Issue number | 19 |

DOIs | |

State | Published - May 12 2014 |

## ASJC Scopus subject areas

- Physics and Astronomy (miscellaneous)