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 Experiment Videos

CT image registration in sinogram space.

Weihua Mao1, Tianfang Li, Nicole Wink

  • 1Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305-5847, USA.

Medical Physics
|October 12, 2007
PubMed
Summary
This summary is machine-generated.

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

Self-Attention Convolutional Neural Network for Improved MR Image Reconstruction.

Information sciences·2020
Same author

miR-365b regulates the development of non-small cell lung cancer via GALNT4.

Experimental and therapeutic medicine·2020
Same author

Deep Learning-Based Spectral Unmixing for Optoacoustic Imaging of Tissue Oxygen Saturation.

IEEE transactions on medical imaging·2020
Same author

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis.

IEEE transactions on medical imaging·2020
Same author

Corrigendum: Neurotransmitters as Modulators of Neural Progenitor Cell Proliferation During Mammalian Neocortex Development.

Frontiers in cell and developmental biology·2020
Same author

Accelerating quantitative MR imaging with the incorporation of B<sub>1</sub> compensation using deep learning.

Magnetic resonance imaging·2020
Same journal

A novel optical respiratory gating system with a hybrid phase-amplitude algorithm for spot-scanning proton therapy.

Medical physics·2026
Same journal

Gamma Knife treatment planning using knowledge-based reinforcement learning.

Medical physics·2026
Same journal

Development and characterization of a novel, small animal external beam irradiator using a clinical high dose rate brachytherapy source.

Medical physics·2026
Same journal

Deep learning-based dose prediction for MR-guided prostate SIB: Supporting rapid feasibility assessment and adaptive editing margin selection.

Medical physics·2026
Same journal

Surface-guided analysis of breast shape changes during postoperative radiotherapy by using a functional map framework.

Medical physics·2026
Same journal

Monte Carlo assessment of a treatment planning system for intraoperative radiotherapy in the presence of tissue heterogeneities.

Medical physics·2026
See all related articles

This study introduces a novel algorithm for sinogram registration, directly linking object motion to changes in CT projection data. This method effectively registers rigid motion before image reconstruction, improving CT imaging with metal artifacts and limited data.

Area of Science:

  • Medical Imaging
  • Image Processing
  • Computational Science

Background:

  • Object displacement in Computed Tomography (CT) scans is typically observed in projection data (sinograms).
  • Accurate registration of motion is crucial for artifact reduction and improved image quality in CT imaging.
  • Existing methods may struggle with metal artifacts or limited projection data, necessitating advanced registration techniques.

Purpose of the Study:

  • To investigate the direct relationship between object motion and alterations in CT projection data (sinogram).
  • To develop and validate a novel algorithm for sinogram registration based on this relationship.
  • To address challenges in registering metallic fiducials within 3D sinogram data.

Main Methods:

  • Investigated the direct correlation between object motion and changes in CT sinogram data.

Related Experiment Videos

  • Developed a novel sinogram registration algorithm utilizing this established relationship.
  • Validated the algorithm using both calculated and experimental data for rigid 2D and 3D motion in parallel and fan beam CT acquisitions.

    • Main Results:

    • The developed sinogram registration technique accurately registers rigid 2D or 3D motion across parallel and fan beam sampling geometries.
    • The algorithm provides a viable solution for the 3D sinogram-based registration of metallic fiducials.
    • Registration performed prior to image reconstruction effectively mitigates issues caused by metal or truncation artifacts.

    Conclusions:

    • The novel sinogram registration algorithm offers a robust method for correcting object motion before CT image reconstruction.
    • This approach is particularly advantageous for CT imaging involving metal artifacts or limited projection data.
    • The algorithm holds significant potential for applications in image-guided radiation therapy and other medical imaging fields.