Quantification of path-length-resolved blood flow changes of human tissue by time-domain diffuse correlation spectroscopy (TD-DCS)

2021 ◽  
Author(s):  
Saeed Samaei ◽  
Piotr Sawosz ◽  
Michal Kacprzak ◽  
Dawid Borycki ◽  
Adam Liebert
Keyword(s):  
Blood Flow ◽  
Time Domain ◽  
Human Tissue ◽  
Path Length ◽  
Correlation Spectroscopy ◽  
Diffuse Correlation Spectroscopy ◽  
Flow Changes

scholarly journals Time-domain diffuse correlation spectroscopy (TD-DCS) for noninvasive, depth-dependent blood flow quantification in human tissue in vivo

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saeed Samaei ◽  
Piotr Sawosz ◽  
Michał Kacprzak ◽  
Żanna Pastuszak ◽  
Dawid Borycki ◽  
...  
Keyword(s):  
Blood Flow ◽  
Time Domain ◽  
Human Tissue ◽  
Correlation Spectroscopy ◽  
Flow Quantification ◽  
Flow Index ◽  
Flow Measurements ◽  
Diffuse Correlation Spectroscopy ◽  
Sensing Applications

AbstractMonitoring of human tissue hemodynamics is invaluable in clinics as the proper blood flow regulates cellular-level metabolism. Time-domain diffuse correlation spectroscopy (TD-DCS) enables noninvasive blood flow measurements by analyzing temporal intensity fluctuations of the scattered light. With time-of-flight (TOF) resolution, TD-DCS should decompose the blood flow at different sample depths. For example, in the human head, it allows us to distinguish blood flows in the scalp, skull, or cortex. However, the tissues are typically polydisperse. So photons with a similar TOF can be scattered from structures that move at different speeds. Here, we introduce a novel approach that takes this problem into account and allows us to quantify the TOF-resolved blood flow of human tissue accurately. We apply this approach to monitor the blood flow index in the human forearm in vivo during the cuff occlusion challenge. We detect depth-dependent reactive hyperemia. Finally, we applied a controllable pressure to the human forehead in vivo to demonstrate that our approach can separate superficial from the deep blood flow. Our results can be beneficial for neuroimaging sensing applications that require short interoptode separation.

Monitoring blood flow changes induced by nicotinamide injection in mouse leg using diffuse correlation spectroscopy

2014 ◽  
Author(s):  
Songfeng Han ◽  
Hyun Jin Kim ◽  
Ki Won Jung ◽  
Halley F. Tsai ◽  
Ashley R. Proctor ◽  
...  
Keyword(s):  
Blood Flow ◽  
Correlation Spectroscopy ◽  
Diffuse Correlation Spectroscopy ◽  
Flow Changes

scholarly journals Optimization of time domain diffuse correlation spectroscopy parameters for measuring brain blood flow

2021 ◽  
Vol 8 (03) ◽  
Author(s):  
Dibbyan Mazumder ◽  
Melissa M. Wu ◽  
Nisan Ozana ◽  
Davide Tamborini ◽  
Maria Angela Franceschini ◽  
...  
Keyword(s):  
Blood Flow ◽  
Time Domain ◽  
Correlation Spectroscopy ◽  
Brain Blood Flow ◽  
Diffuse Correlation Spectroscopy

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

2020 ◽  
Author(s):  
Chien-Sing Poon ◽  
Feixiao Long ◽  
Ulas Sunar
Keyword(s):  
Blood Flow ◽  
Deep Learning ◽  
Real Time ◽  
Learning Model ◽  
Correlation Spectroscopy ◽  
Flow Quantification ◽  
Tissue Blood Flow ◽  
Diffuse Correlation Spectroscopy ◽  
Flow Changes ◽  
Deep Learning Model

ABSTRACTDiffuse 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.

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