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

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

You might also read

Related Articles

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

Sort by
Same author

Imaging lymphatic function and inflammation response through hypoxia via endogenous biomarker.

Journal of biomedical optics·2025
Same author

Wavelet-informed deep video denoising for Cherenkov imaging of radiation therapy.

Optics letters·2025
Same author

Intermediate tissue oxygen level is required to observe murine FLASH skin sparing.

bioRxiv : the preprint server for biology·2025
Same author

Cherenkov emission in realistic optical body phantoms to study effects of skin tone on imaging delivery technique.

Physics in medicine and biology·2025
Same author

FLASH effect is diminished by daily fractionation of electron RT in mouse skin.

Physics in medicine and biology·2025
Same author

Emerging uses of 5-aminolevulinic-acid-induced protoporphyrin IX in medicine: a review of multifaceted, ubiquitous, molecular diagnostic, therapeutic, and theranostic opportunities.

Journal of biomedical optics·2025

Related Experiment Video

Updated: Jul 4, 2026

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

Implementation of a computationally efficient least-squares algorithm for highly under-determined three-dimensional

Phaneendra K Yalavarthy1, Daniel R Lynch, Brian W Pogue

  • 1Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755, USA.

Medical Physics
|June 20, 2008
PubMed
Summary
This summary is machine-generated.

A new generalized least-squares (GLS) method speeds up 3D diffuse optical tomography reconstruction by four times. This method improves quantitative accuracy and reduces peripheral error in optical imaging, enhancing target quantification.

More Related Videos

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
12:24

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging
16:44

Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging

Published on: June 2, 2009

Related Experiment Videos

Last Updated: Jul 4, 2026

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions
13:43

Correlative Microscopy for 3D Structural Analysis of Dynamic Interactions

Published on: June 24, 2013

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers
12:24

Computed Tomography-guided Time-domain Diffuse Fluorescence Tomography in Small Animals for Localization of Cancer Biomarkers

Published on: July 17, 2012

Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging
16:44

Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging

Published on: June 2, 2009

Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Computational Science

Background:

  • Three-dimensional (3D) diffuse optical tomography (DOT) is a complex inverse problem, often nonlinear and ill-posed.
  • Traditional DOT reconstruction requires regularization for stable solutions, but can suffer from slow computation and poor quantitative accuracy.
  • Existing methods like Levenberg-Marquardt (LM) and Tikhonov regularization face challenges in speed and accuracy for under-determined problems.

Purpose of the Study:

  • To implement and validate a computationally efficient generalized least-squares (GLS) minimization method for 3D DOT.
  • To analytically derive and numerically confirm an efficient GLS formulation using the Sherman-Morrison-Woodbury identity.
  • To compare the performance of the proposed GLS method against conventional methods like LM for 3D image reconstruction.

Main Methods:

  • Implemented a generalized least-squares (GLS) minimization incorporating weight matrices for data-model misfit and optical properties.
  • Derived an efficient GLS alternative form using the Sherman-Morrison-Woodbury identity, proving analytical and numerical equivalence.
  • Developed equivalent alternative forms for Levenberg-Marquardt (LM) and Tikhonov minimization methods.

Main Results:

  • The GLS method demonstrated up to a fourfold increase in computation speed per iteration for under-determined 3D imaging problems.
  • GLS reconstruction reduced peripheral image errors and improved the quantification of local interior regions by 20% compared to LM methods.
  • The method proved effective when the ratio of imaging parameters to measurements exceeds two, particularly with characterized detector noise.

Conclusions:

  • The developed GLS method offers a significant speed enhancement and improved quantitative accuracy for 3D diffuse optical tomography.
  • This approach addresses limitations in current reconstruction algorithms, leading to better recovery of optical properties and target characteristics.
  • The GLS method shows promise for advancing quantitative 3D optical imaging applications.