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Deep learning techniques for diffuse correlation spectroscopy: A review
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DOI：10.1142/S1793545825300101.png)
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Abstract
En 中文
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 noninvasiveness, 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 the correlation diffusion equation to fit the measured autocorrelation function g2, 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.
Keywords:
Diffuse correlation spectroscopy
deep learning
blood flow monitoring
Journal
J
IF:
2.2
Papers:
59
Citations:
1.1K