Why the study?
The utility of a long short-term memory architecture for quantifying blood flow index in diffuse correlation spectroscopy was investigated to evaluate blood flow measurement.
An LSTM architecture improves the accuracy and computational speed of blood flow index quantification in diffuse correlation spectroscopy, potentially enabling continuous real-time monitoring.
LSTM-based BFI quantification shows promise in phantoms and in vivo; leaves open clinical translation pending prospective validation.
Diffuse correlation spectroscopy (DCS) is a noninvasive technique that derives blood flow information from measurements of the temporal intensity fluctuations of multiply scattered light. Blood flow index (BFI) and especially its variation was demonstrated to be approximately proportional to absolute blood flow. We investigated and assessed the utility of a long short-term memory (LSTM) architecture for quantification of BFI in DCS. Phantom and in vivo experiments were established to measure normalized intensity autocorrelation function data. Improved accuracy and faster computational time were gained by the proposed LSTM architecture. The results support the notion of using proposed LSTM architecture for quantification of BFI in DCS. This approach would be especially useful for continuous real-time monitoring of blood flow.
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Li et al. (2021) studied this question.
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