Blood flow estimation via numerical integration of temporal autocorrelation function in diffuse correlation spectroscopy. (July 2022)

• Record Type: Journal Article
• Authors: Seong, Myeongsu; Oh, Yoonho; Lee, Kijoon; Kim, Jae G.
• Is Part Of: Computer methods and programs in biomedicine. Volume 222(2022)
• Publication Date: 2022-07
• Subjects: Blood flow -- Diffuse correlation spectroscopy -- Numerical integration; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106933
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Numerical-integration-based algorithms for diffuse correlation spectroscopy (DCS), an optical method for blood flow monitoring, are proposed and validated. Thresholding of g1, an electric-field autocorrelaion function of DCS, is suggested to further improve the suggested methods. Averaged speed of one of the suggested methods, INISg1 with g1 thresholding, even outperforms a deep-learning-based signal processing method in DCS without time-consuming model training procedure. The algorithms were ported and tested on Arduino to show the feasibility of moderate and high-speed DCS signal processing capability using low-cost, small-sized microprocessors. Abstract: Background and Objective: 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. Methods: We use the following methods: the inverse of K 2 (IK2)—based on the framework of diffuse speckle contrast analysis—and the inverse of the numerical integration of squared g 1 (INISg1) which, based on the normalized electric field autocorrelation curve, is more simplified than IK2. In addition, g 1 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. Results: 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 g 1 thresholding was applied, our results showed that INISg1 outperformed IK2. It was further confirmed that INISg1 with g 1 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. Conclusion: Our findings strongly indicate that INISg1 with g 1 thresholding could be an alternative approach to derive relative blood flow information via DCS, which may contribute to the simplification of DCS methodologies.