Why the study?
Traditional curve fitting of analytical or Monte Carlo models to extract blood flow changes in diffuse correlation spectroscopy is computationally demanding and less accurate at low signal-to-noise ratios.
A novel deep learning model significantly accelerates blood flow quantification in diffuse correlation spectroscopy, enabling real-time bedside monitoring.
May enhance DCS blood flow accuracy at low SNR; leaves open clinical adoption pending validation.
Diffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method. The proposed deep learning inverse model will enable real-time and accurate tissue blood flow quantification with the DCS technique.
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Poon et al. (2020) studied this question.
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