Jove
Visualize
Contact Us

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...
Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...

You might also read

Related Articles

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

Sort by
Same author

Image blurring of the test section boundary in a laser specklegram technique measuring temperature gradients of the compressible medium.

Applied optics·2010
Same author

Synchronization of a laser diffraction drop sizing technique with intermittent spray systems.

Applied optics·2010
Same author

Tomographic identification of gas bubbles in two-phase flows with the combined use of the algebraic reconstruction technique and the genetic algorithm.

Optics letters·2007
Same author

Tomographic-image reconstruction using a hybrid genetic algorithm.

Optics letters·1997
Same author

Otogenic tetanus: case report and literature review.

The Journal of laryngology and otology·1981
Same author

Medial canthoplasty: early and delayed repair.

The Laryngoscope·1981
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 Experiment Video

Updated: Jun 19, 2026

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research
15:18

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Published on: January 12, 2013

Optical tomography using a genetic algorithm.

K D Kihm, D P Lyons

    Optics Letters
    |October 31, 2009
    PubMed
    Summary
    This summary is machine-generated.

    A novel tomographic image reconstruction method utilizes a genetic algorithm (GA) for robust optimization. This approach shows promise for improving reconstructions, especially with limited projection data.

    More Related Videos

    Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography
    11:45

    Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography

    Published on: May 26, 2015

    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: Jun 19, 2026

    Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research
    15:18

    Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

    Published on: January 12, 2013

    Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography
    11:45

    Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography

    Published on: May 26, 2015

    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:

    • Computational physics
    • Optimization algorithms
    • Image reconstruction

    Background:

    • Tomographic imaging is crucial for various scientific fields.
    • Existing methods face challenges, particularly with limited projection data.
    • Genetic algorithms offer robust optimization capabilities.

    Purpose of the Study:

    • To introduce a new tomographic image reconstruction method.
    • To evaluate the efficacy of a genetic algorithm (GA)-based approach.
    • To address limitations of current tomographic techniques.

    Main Methods:

    • Development of a GA-based tomography algorithm.
    • Reconstruction of a reference density field using interferometric projection data.
    • Application of genetic principles for optimization.

    Main Results:

    • Successful reconstruction of an axisymmetric density field.
    • Demonstration of GA's potential in overcoming existing tomographic limitations.
    • Promising results for limited projection scenarios.

    Conclusions:

    • GA-based tomography is a viable and promising technique.
    • The method shows potential for enhanced image reconstruction accuracy.
    • Further investigation is warranted for complex applications.