Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy

Chien-Sing Poon; Feixiao Long; Ulas Sunar

doi:10.1364/BOE.402508

Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy

Biomed Opt Express. 2020 Sep 14;11(10):5557-5564. doi: 10.1364/BOE.402508. eCollection 2020 Oct 1.

Authors

Chien-Sing Poon¹, Feixiao Long², Ulas Sunar¹

Affiliations

¹ Department of Biomedical Engineering, Wright State University, 207 Russ Engineering Center, 3640 Colonel Glenn Hwy., Dayton, OH 45435, USA.
² Beijing QED Technique Co., Ltd., Beijing, China.

Abstract

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.