Blood flow estimation via numerical integration of temporal autocorrelation function in diffuse correlation...

Diffuse correlation spectroscopy (DCS) is an optical technique widely used to monitor blood flow. Recently, efforts have been made to derive new signal processing methods to minimize the systems used and shorten the signal processing time. Herein, we propose alternative approaches to obtain blood flow information via DCS by numerically integrating the temporal autocorrelation curves.

We use the following methods: the inverse of K² (IK2)-based on the framework of diffuse speckle contrast analysis-and the inverse of the numerical integration of squared g₁ (INISg1) which, based on the normalized electric field autocorrelation curve, is more simplified than IK2. In addition, g₁ thresholding is introduced to further reduce computational time and make the suggested methods comparable to the conventional nonlinear fitting approach. To validate the feasibility of the suggested methods, studies using simulation, liquid phantom, and in vivo settings were performed. In the meantime, the suggested methods were implemented and tested on three types of Arduino (Arduino Due, Arduino Nano 33 BLE Sense, and Portenta H7) to demonstrate the possibility of miniaturizing the DCS systems using microcotrollers for signal processing.

The simulation and experimental results confirm that both IK2 and INISg1 are sufficiently relevant to capture the changes in blood flow information. More interestingly, when g₁ thresholding was applied, our results showed that INISg1 outperformed IK2. It was further confirmed that INISg1 with g₁ thresholding implemented on a PC and Portenta H7, an advanced Arduino board, performed faster than did the deep learning-based, state-of-the-art processing method.

Our findings strongly indicate that INISg1 with g₁ thresholding could be an alternative approach to derive relative blood flow information via DCS, which may contribute to the simplification of DCS methodologies.