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

9.7K
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...
9.7K

You might also read

Related Articles

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

Sort by
Same author

Towards quality control and harmonization of deep learning CT radiomics: An in-silico feasibility study with virtual colorectal liver metastases.

Medical physics·2026
Same author

ZS4D: Zero-Shot Self-Similarity-Steered Denoiser for Volumetric Photon-Counting CT.

IEEE transactions on radiation and plasma medical sciences·2026
Same author

Dual-Domain Denoising Diffusion Probabilistic Model for Metal Artifact Reduction.

IEEE transactions on radiation and plasma medical sciences·2026
Same author

Oxygenation status with left lateral vs supine position in bariatric patients undergoing esophagogastroduodenoscopy (EGD): safe or not?

Surgical endoscopy·2026
Same author

Improving the efficiency of normalized metal artifact reduction via a unified forward projection.

Physics in medicine and biology·2026
Same author

IFI16 senses and protects stalled replication forks.

Molecular cell·2026
Same journal

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026
Same journal

Millisecond photon-to-photon latency and high-speed volumetric projection system for optogenetics.

Optics express·2026
Same journal

Polarization-encoded coaxial structured light for high-precision 3D surface profilometry.

Optics express·2026
Same journal

Discrete freeform optical design based on collaborative optimization of point cloud and local normals.

Optics express·2026
Same journal

Ultrafast ghost imaging with 25 GHz speckle switching and wavelength-division multiplexing.

Optics express·2026
Same journal

Atomic vapor cells fabricated by femtosecond laser welding of standard-optical-quality glass.

Optics express·2026
See all related articles

Related Experiment Video

Updated: Apr 15, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.8K

Raw data normalization for a multi source inverse geometry CT system.

Jongduk Baek, Bruno De Man, Daniel Harrison

    Optics Express
    |April 4, 2015
    PubMed
    Summary
    This summary is machine-generated.

    A new raw data normalization algorithm effectively removes artifacts in multi-source inverse-geometry CT (MS-IGCT) imaging caused by x-ray source intensity fluctuations. This method preserves image resolution and noise robustness, improving image quality in MS-IGCT systems.

    More Related Videos

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
    05:05

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

    Published on: November 23, 2019

    8.6K
    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
    09:21

    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

    Published on: February 18, 2015

    12.7K

    Related Experiment Videos

    Last Updated: Apr 15, 2026

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.8K
    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
    05:05

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

    Published on: November 23, 2019

    8.6K
    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
    09:21

    Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

    Published on: February 18, 2015

    12.7K

    Area of Science:

    • Medical Imaging
    • Computed Tomography
    • Image Reconstruction

    Background:

    • Multi-source inverse-geometry CT (MS-IGCT) systems offer advantages but face challenges with random x-ray source intensity fluctuations.
    • Conventional normalization methods are inadequate for MS-IGCT due to limited source illumination of reference channels, leading to artifacts.

    Purpose of the Study:

    • To develop and validate a raw data normalization algorithm for MS-IGCT systems.
    • To mitigate image artifacts arising from x-ray source intensity fluctuations.

    Main Methods:

    • A novel raw data normalization algorithm was proposed for MS-IGCT systems.
    • The algorithm's efficacy was evaluated using computer simulations (water, Shepp-Logan phantoms) and experimental data (PMMA, rabbit phantoms).
    • Image resolution and noise robustness were assessed using Modulation Transfer Function (MTF) and standard deviation analysis.

    Main Results:

    • The proposed normalization method effectively removed high-frequency and ring artifacts observed in uncorrected MS-IGCT images.
    • Simulations and experimental data demonstrated significant artifact reduction without introducing new distortions.
    • Image resolution was maintained, and the method showed robustness against noise.

    Conclusions:

    • The developed raw data normalization algorithm is effective in correcting artifacts caused by source intensity fluctuations in MS-IGCT.
    • This technique improves the diagnostic quality of MS-IGCT images without compromising resolution or noise performance.
    • The method offers a viable solution for enhancing the reliability of MS-IGCT imaging.