A N th-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 a th-order li...
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| Published in: | Applied physics letters Vol. 105; no. 13; p. 133702 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
29.09.2014
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| ISSN: | 0003-6951 |
| Online Access: | Get more information |
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| Summary: | 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
th-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in
tissues with arbitrary geometries for extraction of BFI (i.e.,
). The purpose of this study is to extend the capability of the
th-order linear algorithm for extracting BFI in
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
in the brain layer with a step decrement of 10% while maintaining
values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order (
≥ 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
th-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. |
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| ISSN: | 0003-6951 |
| DOI: | 10.1063/1.4896992 |