ArticlePloS one2022
Two-layer analytical model for estimation of layer thickness and flow using Diffuse Correlation Spectroscopy.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- Noninvasive estimation of superficial layer thickness using multi-channel diffuse correlation spectroscopy.Biomedical optics express · 2025Article
- Cerebral blood flow monitoring using a deep learning implementation of the two-layer diffuse correlation spectroscopy analytical model with a 512 × 512 SPAD array.Neurophotonics · 2025Article
- Non-ergodicity and noise-corrected diffuse speckle contrast analysis in deep tissue blood flow measurement.Biomedical optics express · 2025Article
- Modified Beer-Lambert algorithm to measure pulsatile blood flow, critical closing pressure, and intracranial hypertension.Biomedical optics express · 2024Article
- Quantification of blood flow index in diffuse correlation spectroscopy using a robust deep learning method.Journal of biomedical optics · 2024Article
- Comparison of diffuse correlation spectroscopy analytical models for measuring cerebral blood flow in adults.Journal of biomedical optics · 2023Article
- Non-invasive diffuse optical monitoring of cerebral physiology in an adult swine-model of impact traumatic brain injury.Biomedical optics express · 2023Article
- Using depth-enhanced diffuse correlation spectroscopy and near-infrared spectroscopy to isolate cerebral hemodynamics during transient hypotension.Neurophotonics · 2023Article
- Optimizing a two-layer method for hybrid diffuse correlation spectroscopy and frequency-domain diffuse optical spectroscopy cerebral measurements in adults.Neurophotonics · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Diffuse correlation spectroscopy (DCS) has been widely explored for its ability to measure cerebral blood flow (CBF), however, mostly under the assumption that the human head is homogenous. In addition to CBF, knowledge of extracerebral layers, such as skull thickness, can be informative and crucial for patient with brain complications such as traumatic brain injuries. To bridge the gap, this study explored the feasibility of simultaneously extracting skull thickness and flow in the cortex layer using DCS. We validated a two-layer analytical model that assumed the skull as top layer with a finite thickness and the brain cortex as bottom layer with semi-infinite geometry. The model fitted for thickness of the top layer and flow of the bottom layer, while assumed other parameters as constant. The accuracy of the two-layer model was tested against the conventional single-layer model using measurements from custom made two-layer phantoms mimicking skull and brain. We found that the fitted top layer thickness at each source detector (SD) distance is correlated with the expected thickness. For the fitted bottom layer flow, the two-layer model fits relatively consistent flow across all top layer thicknesses. In comparison, the conventional one-layer model increasingly underestimates the bottom layer flow as top layer thickness increases. The overall accuracy of estimating first layer thickness and flow depends on the SD distance in relationship to first layer thickness. Lastly, we quantified the influence of uncertainties in the optical properties of each layer. We found that uncertainties in the optical properties only mildly influence the fitted thickness and flow. In this work we demonstrate the feasibility of simultaneously extracting of layer thickness and flow using a two-layer DCS model. Findings from this work may introduce a robust and cost-effective approach towards simultaneous bedside assessment of skull thickness and cerebral blood flow.
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