Open AccessQuantification of blood flow index in diffuse correlation spectroscopy using long short-term memory architecture
Author(s) -
Zhe Li,
Qisi Ge,
Jinchao Feng,
Kebin Jia,
Jing Zhao
Publication year - 2021
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.423777
Subject(s) - blood flow , autocorrelation , computer science , intensity (physics) , diffuse optical imaging , measure (data warehouse) , spectroscopy , imaging phantom , biological system , biomedical engineering , optics , materials science , artificial intelligence , mathematics , physics , data mining , statistics , medicine , iterative reconstruction , cardiology , biology , quantum mechanics
Diffuse correlation spectroscopy (DCS) is a noninvasive technique that derives blood flow information from measurements of the temporal intensity fluctuations of multiply scattered light. Blood flow index (BFI) and especially its variation was demonstrated to be approximately proportional to absolute blood flow. We investigated and assessed the utility of a long short-term memory (LSTM) architecture for quantification of BFI in DCS. Phantom and in vivo experiments were established to measure normalized intensity autocorrelation function data. Improved accuracy and faster computational time were gained by the proposed LSTM architecture. The results support the notion of using proposed LSTM architecture for quantification of BFI in DCS. This approach would be especially useful for continuous real-time monitoring of blood flow.