Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Ultrasensitive Chemical Detection Using Integrating Cavity-Enhanced Raman Spectroscopy.

Analytical chemistry·2026
Same author

Sub-microsecond optical measurements of cell membrane charging and lesioning by pulsed electric fields.

Bioelectrochemistry (Amsterdam, Netherlands)·2026
Same author

High-fidelity microsecond-scale cellular imaging using two-axis compressed streak imaging fluorescence microscopy.

ArXiv·2025
Same author

Resolving nanosecond kinetics of the optical membrane potential in pulsed electric fields.

Bioelectrochemistry (Amsterdam, Netherlands)·2025
Same author

Thermal damage induced changes in optical properties of the porcine dermis.

Journal of biomedical optics·2025
Same author

3D-printed fiber-bundle fluorescence microscope for quantifying single-cell responses to high-power radiofrequency sources.

Biomedical optics express·2025
See all related articles

Related Experiment Video

Updated: Nov 11, 2025

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy
09:25

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy

Published on: August 22, 2018

12.9K

Machine learning estimation of tissue optical properties.

Brett H Hokr1, Joel N Bixler2

  • 1Radiance Technologies Inc, 310 Bob Heath Dr., Huntsville, AL, 35805, USA. brett.hokr@radiancetech.com.

Scientific Reports
|March 23, 2021
PubMed
Summary
This summary is machine-generated.

This study presents a novel method using neural networks to accurately determine biological tissue optical properties from light scattering data. The technique is robust and suitable for real-time in vivo measurements.

More Related Videos

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis
10:35

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

Published on: October 17, 2016

8.1K
Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure
10:22

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

Published on: February 12, 2018

10.9K

Related Experiment Videos

Last Updated: Nov 11, 2025

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy
09:25

Agarose-based Tissue Mimicking Optical Phantoms for Diffuse Reflectance Spectroscopy

Published on: August 22, 2018

12.9K
Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis
10:35

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

Published on: October 17, 2016

8.1K
Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure
10:22

Fabrication and Characterization of Optical Tissue Phantoms Containing Macrostructure

Published on: February 12, 2018

10.9K

Area of Science:

  • Biomedical Optics
  • Medical Imaging Technology
  • Computational Biology

Background:

  • Accurate in vivo measurement of biological tissue optical properties remains a significant challenge.
  • Understanding tissue optics is crucial for various medical applications, including diagnostics and treatment planning.

Purpose of the Study:

  • To develop and validate a novel technique for extracting tissue optical properties.
  • To enable dynamic, real-time in vivo measurements of these properties.

Main Methods:

  • Utilized a Monte Carlo simulation inverted by a 5-layer fully connected neural network.
  • Extracted tissue optical properties from statistical moments of spatio-temporal light response.
  • Validated the method across a wide parameter space on a homogeneous tissue model.

Main Results:

  • Demonstrated high accuracy of the neural network-based method for determining optical properties.
  • Showed the method's insensitivity to neural network parameter selection.
  • Proposed a feasible experimental setup for real-time in vivo measurements.

Conclusions:

  • The developed technique offers a robust and accurate solution for in vivo tissue optical property measurement.
  • The proposed experimental setup paves the way for real-time clinical applications.
  • This advancement has significant implications for optical diagnostics and therapies.