Cerebral blood flow monitoring using a deep learning implementation of the two-layer diffuse correlation spectroscopy analytical model with a 512 × 512 SPAD array.

Significance: Multilayer (two- and three-layer) diffuse correlation spectroscopy (DCS) models improve cerebral blood flow index (CBFi) measurement sensitivity and mitigate interference from extracerebral tissues. However, their reliance on multiple predefined parameters (e.g., layer thickness and optical properties) and high computational load limit their feasibility for real-time bedside monitoring. Aim: We aim to develop a fast, accurate DCS data processing method based on the two-layer DCS analytical model, enabling real-time cerebral perfusion monitoring with enhanced brain sensitivity. Approach: We employed deep learning (DL) to accelerate DCS data processing. Unlike previous DCS networks trained on single-layer models, our network learns from the two-layer DCS analytical model, capturing extracerebral versus cerebral dynamics. Realistic noise was estimated from subject-specific baseline measurements using a Results: The proposed method bypasses traditional curve-fitting and achieves real-time monitoring of CBF changes at 35 mm separation for the first time with a DL approach. Validation on MC simulations shows superior accuracy in relative CBFi estimation (4.1% error versus 12.7% for single-exponential fitting) and significantly enhanced CBFi sensitivity (86.5% versus 57.7%). Although the two-layer analytical fitting offers optimal performance, it depends on strict assumptions and preconditions, and its computational complexity makes it time-consuming and unsuitable for real-time monitoring applications.In addition, our method minimizes the influence of superficial blood flow and is 750-fold faster than single-exponential fitting in a realistic scenario. Conclusions: This study demonstrates that integrating DL with the two-layer DCS analytical model enables accurate, real-time cerebral perfusion monitoring without sacrificing depth sensitivity. The proposed method enhances CBFi sensitivity and recovery accuracy, supporting future deployment in bedside neuro-monitoring applications.