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

Theoretical framework for a dynamic cone-beam reconstruction algorithm based on a dynamic particle model.

Pierre Grangeat1, Anne Koenig, Thomas Rodet

  • 1Laboratoire d'Electronique et de Technologie de l'Information (LETI), Département Systèmes pour l'Information et la Santé (DSIS), Commissariat à l'Energie Atomique (CEA), 38054 Grenoble, France. Pierre.Grangeat@cea.fr

Physics in Medicine and Biology
|September 5, 2002
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

ENDOSWIR clinical proof of concept: a reflectance-based multispectral imaging device for head and neck cancer detection.

Biomedical optics express·2026
Same author

Functional Genomics Screening in <i>Chlamydomonas reinhardtii</i> Maps the Genetic Landscape of Tolerance to Paraquat and Diuron.

Environmental science & technology·2026
Same author

A Unified Framework for Matrix Backpropagation.

IEEE transactions on neural networks and learning systems·2025
Same author

Compact Colocated Bimodal EEG/fNIRS Multi-Distance Sensor.

Sensors (Basel, Switzerland)·2025
Same author

Using EEG to measure MMN during hypnosis: A preliminary study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Radon-222 signatures of atmospheric dynamics in the Pech Merle Painted Cave, France: Consequences for management and conservation.

The Science of the total environment·2024
Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026
Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026
Same journal

Corrigendum: Measured and Monte Carlo simulated electron backscatter to the monitor chamber for the varian TrueBeam linac (2016<i>Phys. Med. Biol</i>.<b>61</b>8779).

Physics in medicine and biology·2026
Same journal

Corrigendum: 3D range-modulator for scanned particle therapy: development, Monte Carlo simulations and experimental evaluation (2017<i>Phys. Med. Biol</i>.<b>62</b>7075).

Physics in medicine and biology·2026
Same journal

Recent progress in applications of computing to radiotherapy (ICCR 2016).

Physics in medicine and biology·2026
Same journal

Novel TMS coils designed using an inverse boundary element method.

Physics in medicine and biology·2026
See all related articles

This study introduces a dynamic particle model for dynamic 3D CT imaging, reducing radiation dose while maintaining image quality. The novel cone-beam reconstruction algorithm addresses motion artifacts in fast-scanning CT.

Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Physics

Background:

  • Dynamic 3D CT requires advanced reconstruction algorithms for high temporal resolution.
  • Faster scanners increase radiation dose per rotation to maintain signal-to-noise ratio.
  • Motion artifacts are a challenge in dynamic CT imaging.

Purpose of the Study:

  • To develop a dynamic cone-beam reconstruction algorithm for dynamic 3D CT.
  • To reduce radiation dose by acquiring data over multiple half-turns.
  • To compensate for object evolution and motion artifacts using a dynamic particle model.

Main Methods:

  • Introduction of a dynamic particle model to describe object evolution.
  • Development of a dynamic CT acquisition model.

Related Experiment Videos

  • Proposal and explanation of a dynamic cone-beam reconstruction algorithm.

    • Main Results:

    • Preliminary results on simulated data demonstrate the feasibility of the approach.
    • The proposed method aims to reduce radiation dose while preserving signal-to-noise ratio.
    • The dynamic particle model helps compensate for time evolution and motion.

    Conclusions:

    • The proposed dynamic cone-beam reconstruction algorithm offers a promising solution for dynamic 3D CT.
    • This approach facilitates dose reduction in high-temporal-resolution CT imaging.
    • Further validation with real-world data is warranted.