Deep learning techniques for diffuse correlation spectroscopy : a review

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Pan, Mingliang and Wang, Quan and Zhang, Yuanzhe and Li, Chenxu and Li, David (2025) Deep learning techniques for diffuse correlation spectroscopy : a review. Journal of Innovative Optical Health Sciences. (https://doi.org/10.1142/S1793545825300101)

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Abstract

Diffuse correlation spectroscopy (DCS) is an optical technology for extracting blood flow index (BFi) by measuring intensity fluctuations of the back-scattered light emitted from tissues. The remarkable characteristics of DCS, such as its non-invasiveness, deep penetration depth, and cost-effectiveness, have led to its widespread application for human health evaluation. However, traditional DCS data processing utilizes the analytical solution of correlation diffusion equation to fit the measured autocorrelation function g_2, which is computationally demanding and susceptible to noise, especially when using the multi-layer analytical models to detect cerebral BFi. These drawbacks limit its further application for human BFi monitoring. To accelerate BFi extraction, deep learning (DL) techniques have been introduced. DL assisted DCS has demonstrated fast and improved noise robustness for BFi extraction compared to traditional curve fitting methods. In this review, the development of DL models for DCS data processing, current challenges and outlooks will be discussed, aiming to provide a reference for further promotion of the development of DL-based DCS technology for human health analysis.

ORCID iDs

Pan, Mingliang ORCID logoORCID: https://orcid.org/0009-0001-9732-8963, Wang, Quan ORCID logoORCID: https://orcid.org/0009-0005-2936-825X, Zhang, Yuanzhe ORCID logoORCID: https://orcid.org/0009-0004-5107-3856, Li, Chenxu and Li, David ORCID logoORCID: https://orcid.org/0000-0002-6401-4263;
CORE (COnnecting REpositories)