Deep-learning-based separation of shallow and deep layer blood flow rates in diffuse correlation spectroscopy

Diffuse correlation spectroscopy faces challenges concerning the contamination of cutaneous and deep tissue blood flow. We propose a long short-term memory network to directly quantify the flow rates of shallow and deep-layer tissues. By exploiting the different contributions of shallow and deep-lay...

Full description

Saved in:
Bibliographic Details
Published in:Biomedical optics express 2023-10, Vol.14 (10), p.5358-5375
Main Authors: Nakabayashi, Mikie, Liu, Siwei, Broti, Nawara Mahmood, Ichinose, Masashi, Ono, Yumie
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Diffuse correlation spectroscopy faces challenges concerning the contamination of cutaneous and deep tissue blood flow. We propose a long short-term memory network to directly quantify the flow rates of shallow and deep-layer tissues. By exploiting the different contributions of shallow and deep-layer flow rates to auto-correlation functions, we accurately predict the shallow and deep-layer flow rates (RMSE = 0.047 and 0.034 ml/min/100 g of simulated tissue, R 2 = 0.99 and 0.99, respectively) in a two-layer flow phantom experiment. This approach is useful in evaluating the blood flow responses of active muscles, where both cutaneous and deep-muscle blood flow increase with exercise.
ISSN:2156-7085
2156-7085
DOI:10.1364/BOE.498693