Abstract
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 a Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αDB ). The purpose of this study is to extend the capability of the Nth-order linear algorithm for extracting BFI in heterogeneous tissues 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 of αDB in the brain layer with a step decrement of 10% while maintaining αDB values 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. The Nth-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.
Document Type
Article
Publication Date
10-1-2014
Digital Object Identifier (DOI)
http://dx.doi.org/10.1063/1.4896992
Funding Information
This study was supported by a pilot award (G.Y.) from the National Institutes of Health (NIH) P30 #AG028383 and the grants from the American Heart Association (AHA) including BGIA No. 2350015 (G.Y.) and Postdoctoral Fellowship Awards No. 11POST7360020 (Y.S.). The content herein is solely the responsibility of the authors and does not necessarily represent the official views of the NIH and AHA.
Repository Citation
Shang, Yu and Yu, Guoqiang, "A Nth-Order Linear Algorithm for Extracting Diffuse Correlation Spectroscopy Blood Flow Indices in Heterogeneous Tissues" (2014). Biomedical Engineering Faculty Publications. 5.
https://uknowledge.uky.edu/cbme_facpub/5
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Notes/Citation Information
Published in Applied Physics Letters, v. 105, no. 13, article 133702, p. 1-5.
Copyright 2014 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.
The following article appeared in Applied Physics Letters, v. 105, no. 13, article 133702, p. 1-5 and may be found at http://dx.doi.org/10.1063/1.4896992.