Open AccessDeep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy
Author(s) -
Chien-Sing Poon,
Feixiao Long,
Ulaş Sunar
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.402508
Subject(s) - bottleneck , blood flow , monte carlo method , computer science , inverse problem , artificial intelligence , biological system , nonlinear system , inverse , optical flow , flow (mathematics) , optics , algorithm , physics , mathematics , statistics , image (mathematics) , medicine , radiology , mathematical analysis , quantum mechanics , biology , mechanics , embedded system , geometry
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.