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

Statistical image reconstruction for transmission tomography using relaxed ordered subset algorithms.

J S Kole1

  • 1Image Sciences Institute, Department of Nuclear Medicine and Department of Pharmacology and Anatomy, Rudolf Magnus Institute of Neuroscience, UMC Utrecht, Universiteitsweg 100, STR5.203, 3584 CG Utrecht, The Netherlands. j.s.kole@azu.nl

Physics in Medicine and Biology
|March 31, 2005
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

Evaluation of accelerated iterative x-ray CT image reconstruction using floating point graphics hardware.

Physics in medicine and biology·2006
Same author

Parallel statistical image reconstruction for cone-beam x-ray CT on a shared memory computation platform.

Physics in medicine and biology·2005
Same author

Evaluation of the ordered subset convex algorithm for cone-beam CT.

Physics in medicine and biology·2005
Same author

One-step finite-difference time-domain algorithm to solve the Maxwell equations.

Physical review. E, Statistical, nonlinear, and soft matter physics·2003
Same author

Higher-order unconditionally stable algorithms to solve the time-dependent Maxwell equations.

Physical review. E, Statistical, nonlinear, and soft matter physics·2002
Same author

Unconditionally stable algorithms to solve the time-dependent Maxwell equations.

Physical review. E, Statistical, nonlinear, and soft matter physics·2001
Same journal

Impact of apertures on the out-of-field secondary neutron dose in collimated proton pencil-beam scanning.

Physics in medicine and biology·2026
Same journal

Quantifying cardiac deformable image registration accuracy and its dosimetric variability for 4D dose accumulation in stereotactic arrhythmia radioablation.

Physics in medicine and biology·2026
Same journal

Probabilistic modelling of bilateral lymphatic spread in oral cavity squamous cell carcinoma for personalised elective nodal treatment.

Physics in medicine and biology·2026
Same journal

A Monte Carlo simulation tool to analyze breast cancer trial outcomes: application to FAST-Forward trial.

Physics in medicine and biology·2026
Same journal

Effective contrast-enhanced preprocessing for intracranial artery segmentation in digital subtraction angiography.

Physics in medicine and biology·2026
Same journal

Improving Plan Quality in Adaptive Proton Therapy Using an Interactive Dose Modification Tool.

Physics in medicine and biology·2026
See all related articles

Statistical reconstruction methods for X-ray computed tomography (CT) are slow. Applying (under) relaxation to ordered subset algorithms speeds up image reconstruction and improves convergence, making CT more clinically viable.

Area of Science:

  • Medical Imaging
  • Computational Science

Background:

  • Analytical reconstruction methods in X-ray computed tomography (CT) are limited by image quality.
  • Statistical reconstruction methods offer improved image quality but face challenges with long reconstruction times, hindering clinical use.

Purpose of the Study:

  • To reduce reconstruction times for statistical iterative algorithms in CT.
  • To improve the convergence properties of ordered subset algorithms.

Main Methods:

  • Applied (under) relaxation techniques to ordered subset algorithms.
  • Utilized single projection angle subsets to increase image updates per iteration.
  • Investigated two schemes for setting the relaxation parameter.

Main Results:

Related Experiment Videos

  • Achieved comparable noise-to-resolution trade-offs with fewer iterations compared to conventional algorithms.
  • Demonstrated improved convergence, indicated by a lower minimal normalized mean square error (NMSE).
  • Both relaxation parameter schemes resulted in similar minimal NMSE.

Conclusions:

  • The application of (under) relaxation significantly enhances the efficiency and convergence of ordered subset algorithms for CT reconstruction.
  • This method shows promise for enabling routine clinical application of advanced statistical CT reconstruction techniques.