Fast blood flow index reconstruction of diffuse correlation spectroscopy using a back-propagation-free data-driven algorithm.

ABSTRACT: This study introduces a fast and accurate online training method for blood flow index (BFI) and relative BFI (rBFI) reconstruction in diffuse correlation spectroscopy (DCS). We implement rigorous mathematical models to simulate the auto-correlation functions (g ₂) for semi-infinite homogeneous and three-layer human brain models. We implemented a fast online training algorithm known as random vector functional link (RVFL) to reconstruct BFI from noisy g ₂. We extensively evaluated RVFL regarding both speed and accuracy for training and inference. Moreover, we compared RVFL with extreme learning machine (ELM) architecture, a conventional convolutional neural network (CNN), and three fitting algorithms. Results from semi-infinite and three-layer models indi

SUBMITTER: Zang Z 

PROVIDER: S-EPMC11919341 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature