Influence of source-detector separation on diffuse correlation spectroscopy measurements of cerebral blood flow with a multilayered analytical model

Significance: Diffuse correlation spectroscopy (DCS) is an emerging noninvasive optical technology for bedside monitoring of cerebral blood flow. However, extracerebral hemodynamics can significantly influence DCS estimations of cerebral perfusion. Advanced analytical models can be used to remove the contribution of extracerebral hemodynamics; however, these models are highly sensitive to measurement noise. There is a need for an empirical determination of the optimal source-detector separation(s) (SDS) that improves the accuracy and reduces sensitivity to noise in the estimation of cerebral blood flow with these models. Aim: To determine the influence of SDS on solution uniqueness, measurement accuracy, and sensitivity to inaccuracies in model parameters when using the three-layer model to estimate cerebral blood flow with DCS. Approach: We performed a series of in silico simulations on samples spanning a wide range of physiologically-relevant layer optical properties, thicknesses, and flow. Data were simulated at SDS ranging from 0.5 to 3.0 cm using the three-layer solution to the correlation diffusion equation (with and without noise added) and using three-layer slab Monte Carlo simulations. We quantified the influence of SDS on uniqueness, accuracy, and sensitivity to inaccuracies in model parameters using the three-layer inverse model. Results: Two SDS are required to ensure a unique solution of cerebral blood flow index (CBFi). Combinations of 0.5/1.0/1.5 and 2.5 cm provide the optimal choice for balancing the depth penetration with signal-to-noise ratio to minimize the error in CBFi across a wide range of samples with varying optical properties, thicknesses, and dynamics. Conclusions: These results suggest that the choice of SDS is critical for minimizing the estimated error of cerebral blood flow when using the three-layer model to analyze DCS data.