Diffuse correlation spectroscopy (DCS) is a promising technique for brain monitoring as it can provide a continuous signal that is directly related to cerebral blood flow (CBF); however, signal contamination from extracerebral tissue can cause flow underestimations. The goal of this study was to investigate whether a multi-layered (ML) model that accounts for light propagation through the different tissue layers could successfully separate scalp and brain flow when applied to DCS data acquired at multiple source-detector distances. The method was first validated with phantom experiments. Next, experiments were conducted in a pig model of the adult head with a mean extracerebral tissue thickness of 9.8 ± 0.4 mm. Reductions in CBF were measured by ML DCS and computed tomography perfusion for validation; excellent agreement was observed by a mean difference of 1.2 ± 4.6% (CI: -31.1 and 28.6) between the two modalities, which was not significantly different.
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| http://dx.doi.org/10.1364/BOE.7.003659 | DOI Listing |
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