Comparison of diffuse correlation spectroscopy analytical models for cerebral blood flow measurements.

Significance: Although multi-layer diffuse correlation spectroscopy (DCS) analytical models have been proposed to reduce contamination from superficial signals when probing cerebral blood flow index (CBFi), a comprehensive comparison and clear guidance for model selection remain lacking. This report aims to address this gap. Aim: We aim to systematically compare three DCS analytical models: the semi-infinite, two-layer, and three-layer models, with a focus on their fundamental differences, data processing approaches, and the accuracy and reliability of CBFi estimation. We also provide practical recommendations for selecting the most appropriate model based on specific application scenarios to support researchers in applying DCS effectively. Approach: Experimental data were generated by simulating a four-layer slab head model using the Monte Carlo eXtreme toolkit. We evaluated various fitting strategies for three DCS models: early time lag range (ETLR) fitting with or without treating the coherence factor Results: The optimal fitting methods for estimating CBFi are ETLR fitting with a constant Conclusions: This systematic comparison of three DCS analytical models demonstrates the superior ability of multi-layer models to reduce the influence of superficial tissue layers, thereby enhancing CBFi and rCBFi sensitivity relative to the semi-infinite model. We evaluated various fitting strategies, and beyond recommending the optimal approach for each model, we provide practical guidance for selecting the most appropriate model based on specific objectives, experimental conditions, and data analysis requirements. We believe we offer a valuable reference for researchers in the field, supporting informed model selection and highlighting key considerations for the effective application of DCS analytical models.