ANth-order linear algorithm for extracting diffuse correlation spectroscopy blood flow indices in heterogeneous tissues.
Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating aNth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations inhomogenoustissues with arbitrary geometries for extraction of BFI (i.e.,αDB). The purpose of this study is to extend the capability of theNth-order linear algorithm for extracting BFI inheterogeneoustissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different types of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels ofαDBin the brain layer with a step decrement of 10% while maintainingαDBvalues constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order (N≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. TheNth-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.
Department of Biomedical Engineering, University of Kentucky , Lexington, Kentucky 40506, USA.
Yu
Y
Shang
Department of Biomedical Engineering, University of Kentucky , Lexington, Kentucky 40506, USA.
Guoqiang
G
Yu