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Diffuse correlation spectroscopy (DCS) is an emerging noninvasive techniquethat measures the tissue blood flow, by using near-infrared coherentpoint-source illumination to detect spectral changes. While machine learninghas demonstrated significant potential for measuring blood flow index (BFi), anopen question concerning the success of this approach pertains to itsrobustness in scenarios involving deviations between datasets with varyingSignal-to-Noise Ratios (SNRs) originating from diverse clinical applicationsand various setups. This study proposes a transfer learning approach, aims toassess the influence of SNRs on the generalization ability of learned features,and demonstrate the robustness for transfer learning. A synthetic dataset withvarying levels of added noise is utilized to simulate different SNRs. Theproposed network takes a 1x64 autocorrelation curve as input and generates BFiand the correlation parameter beta. The proposed model demonstrates excellentperformance across different SNRs, exhibiting enhanced fitting accuracy,particularly for low SNR datasets when compared with other fitting methods.This highlights its potential for clinical diagnosis and treatment acrossvarious scenarios under different clinical setups.

Enhancing Blood Flow Assessment in Diffuse Correlation Spectroscopy: A Transfer Learning Approach with Noise Robustness Analysis