Numerical approach to quantify depth-dependent blood flow changes in real-time using the diffusion equation with continuous-wave and time-domain diffuse correlation spectroscopy

Michael Helton; Suraj Rajasekhar; Samantha Zerafa; Karthik Vishwanath; Mary-Ann Mycek

doi:10.1364/BOE.469419

Numerical approach to quantify depth-dependent blood flow changes in real-time using the diffusion equation with continuous-wave and time-domain diffuse correlation spectroscopy

Biomed Opt Express. 2022 Dec 20;14(1):367-384. doi: 10.1364/BOE.469419. eCollection 2023 Jan 1.

Authors

Michael Helton¹, Suraj Rajasekhar², Samantha Zerafa³, Karthik Vishwanath^{2

4}, Mary-Ann Mycek^{1

3}

Affiliations

¹ Applied Physics Program, University of Michigan, Ann Arbor, USA.
² Cell, Molecular and Structural Biology Program, Miami University, Oxford, OH, USA.
³ Biomedical Engineering Department, University of Michigan, Ann Arbor, USA.
⁴ Department of Physics, Miami University, Oxford, OH, USA.

Abstract

Diffuse correlation spectroscopy (DCS) is a non-invasive optical technique that can measure brain perfusion by quantifying temporal intensity fluctuations of multiply scattered light. A primary limitation for accurate quantitation of cerebral blood flow (CBF) is the fact that experimental measurements contain information about both extracerebral scalp blood flow (SBF) as well as CBF. Separating CBF from SBF is typically achieved using multiple source-detector channels when using continuous-wave (CW) light sources, or more recently with use of time-domain (TD) techniques. Analysis methods that account for these partial volume effects are often employed to increase CBF contrast. However, a robust, real-time analysis procedure that can separate and quantify SBF and CBF with both traditional CW and TD-DCS measurements is still needed. Here, we validate a data analysis procedure based on the diffusion equation in layered media capable of quantifying both extra- and cerebral blood flow in the CW and TD. We find that the model can quantify SBF and CBF coefficients with less than 5% error compared to Monte Carlo simulations using a 3-layered brain model in both the CW and TD. The model can accurately fit data at a rate of <10 ms for CW data and <250 ms for TD data when using a least-squares optimizer.