Measurement of Tissue Optical Properties: Methods and Theories

1995 ◽  
pp. 233-303 ◽  
Author(s):  
Brian C. Wilson
Keyword(s):  
Optical Properties ◽  
Tissue Optical Properties

scholarly journals Machine learning estimation of tissue optical properties

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Brett H. Hokr ◽  
Joel N. Bixler
Keyword(s):  
Neural Network ◽  
Optical Properties ◽  
Biological Tissues ◽  
Homogeneous Layer ◽  
In Vivo Measurement ◽  
Tissue Optical Properties ◽  
The Neural Network ◽  
Spatio Temporal ◽  
Fully Connected

AbstractDynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. We demonstrate the accuracy of the method across a very wide parameter space on a single homogeneous layer tissue model and demonstrate that the method is insensitive to parameter selection of the neural network model itself. Finally, we propose an experimental setup capable of measuring the required information in real time in an in vivo environment and demonstrate proof-of-concept level experimental results.

scholarly journals Measurement of tissue optical properties by the use of oblique-incidence optical fiber reflectometry

1997 ◽  
Vol 36 (1) ◽  
pp. 136 ◽  
Author(s):  
Shao-Pow Lin ◽  
Lihong Wang ◽  
Steven L. Jacques ◽  
Frank K. Tittel
Keyword(s):  
Optical Properties ◽  
Optical Fiber ◽  
Oblique Incidence ◽  
Tissue Optical Properties

Tissue Optical Properties in Relation to Light Propagation Models and in Vivo Dosimetry

1989 ◽  
pp. 25-42 ◽  
Author(s):  
Brian C. Wilson ◽  
Michael S. Patterson ◽  
Stephen T. Flock ◽  
Douglas R. Wyman
Keyword(s):  
Optical Properties ◽  
Light Propagation ◽  
In Vivo Dosimetry ◽  
Propagation Models ◽  
Tissue Optical Properties

Effect of surface roughness on determination of tissue optical properties obtained by diffusion approximation

2007 ◽  
Vol 46 (17) ◽  
pp. 3649 ◽  
Author(s):  
Meixiu Sun ◽  
Chunping Zhang ◽  
Zhaofeng Hao ◽  
Jianguo Tian
Keyword(s):  
Surface Roughness ◽  
Optical Properties ◽  
Diffusion Approximation ◽  
Tissue Optical Properties ◽  
Effect Of Surface

Measurement and Thermal Dependence of Biological Tissue Optical Properties

2018 ◽  
pp. 355-378 ◽  
Author(s):  
William C. Vogt ◽  
Jennifer K. Barton ◽  
Anant Agrawal ◽  
T. Joshua Pfefer
Keyword(s):  
Optical Properties ◽  
Biological Tissue ◽  
Thermal Dependence ◽  
Tissue Optical Properties

Impact of Changes in Tissue Optical Properties on Near-Infrared Diffuse Correlation Spectroscopy Measures of Skeletal Muscle Blood Flow

2021 ◽  
Author(s):  
Miles F. Bartlett ◽  
Scott M. Jordan ◽  
Dennis M. Hueber ◽  
Michael D. Nelson
Keyword(s):  
Skeletal Muscle ◽  
Blood Flow ◽  
Optical Properties ◽  
Near Infrared ◽  
Muscle Blood Flow ◽  
Reactive Hyperemia ◽  
Correlation Spectroscopy ◽  
Skeletal Muscle Blood Flow ◽  
Diffuse Correlation Spectroscopy ◽  
Tissue Optical Properties

Near-infrared diffuse correlation spectroscopy (DCS) is increasingly utilized to study relative changes in skeletal muscle blood flow. However, most diffuse correlation spectrometers assume that tissue optical properties- such as absorption (μa) and reduced scattering (μ's) coefficients- remain constant during physiological provocations, which is untrue for skeletal muscle. Here, we interrogate how changes in tissue μa and μ's affect DCS calculations of blood flow index (BFI). We recalculated BFI using raw autocorrelation curves and μa/μ's values recorded during a reactive hyperemia protocol in 16 healthy young individuals. First, we show that incorrectly assuming baseline μa and μ's substantially affects peak BFI and BFI slope when expressed in absolute terms (cm2/s, p<0.01) but these differences are abolished when expressed in relative terms (% baseline). Next, to evaluate the impact of physiologic changes in μa and μ's, we compared peak BFI and BFI slope when μa and μ's were held constant throughout the reactive hyperemia protocol versus integrated from a 3s-rolling average. Regardless of approach, group means for peak BFI and BFI slope did not differ. Group means for peak BFI and BFI slope were also similar following ad absurdum analyses, where we simulated supraphysiologic changes in μa/μ's. In both cases, however, we identified individual cases where peak BFI and BFI slope were indeed affected, with this result being driven by relative changes in μa over μ's. Overall, these results provide support for past reports in which μa/μ's were held constant but also advocate for real-time incorporation of μa and μ's moving forward.

Tissue optical properties estimation from cross-polarization OCT data for breast cancer margin assessment

2020 ◽  
Vol 17 (7) ◽  
pp. 075602 ◽  
Author(s):  
Ekaterina V Gubarkova ◽  
Alexander A Moiseev ◽  
Elena B Kiseleva ◽  
Dmitry A Vorontsov ◽  
Sergey S Kuznetsov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Optical Properties ◽  
Cross Polarization ◽  
Margin Assessment ◽  
Tissue Optical Properties
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