Traditionally, diffuse correlation spectroscopy (DCS) derives blood flow (BF) by measuring of the temporal intensity fluctuations of multiply scattered light from a single source-detector pair. In this paper, a multi-wavelength DCS approach was proposed to quantify tissue blood flow and scattering coefficient based on long short-term memory (LSTM) architecture. Phantom experiments were established to measure normalized intensity autocorrelation function data by multi-wavelength DCS system at different velocities and scattering coefficients. The results support the notion of using proposed LSTM architecture for quantification of blood flow and scattering coefficient in DCS.
Diffuse correlation spectroscopy (DCS) derives blood flow index (BFI) by measuring the temporal intensity fluctuations of multiply scattered light. Blood flow index (BFI) and especially its variations were demonstrated to be approximately proportional to absolute blood flow. In this paper, we have proposed a predictive method for calculating blood flow as well as oxygen saturation, based on a deep neural network of long short-term memory (LSTM) architecture. The simulated multiwavelength normalized intensity autocorrelation function data for various blood flows and oxygen saturations were used to train the LSTM architecture. The results validated the feasibility of the proposed method for quantification of blood flow and oxygen saturation simultaneously in DCS. The proposed approach would be an alternative method for oxygen metabolism monitoring.
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