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

An accelerated convergent ordered subsets algorithm for emission tomography.

Ing-Tsung Hsiao1, Anand Rangarajan, Parmeshwar Khurd

  • 1School of Medical Technology, Chang Gung University, Kwei-Shan, Tao-Yuan 333, Taiwan. ihsiao@mail.cgu.edu.tw

Physics in Medicine and Biology
|July 14, 2004
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

A clinically feasible framework to estimate tau pathology and clinical-biological discordance in the Alzheimer's disease spectrum.

Alzheimer's research & therapy·2026
Same author

ADAS-Cog11 performance as a surrogate of tau PET burden in an Asian Alzheimer's disease cohort.

Journal of Alzheimer's disease : JAD·2026
Same author

Longitudinal voxel-based FDG PET assessment of chemotherapy effects on brain metabolism in lung cancer.

Brain research bulletin·2026
Same author

An External Validation Study on Two Pre-Trained Large Language Models for Multimodal Prognostication in Laryngeal and Hypopharyngeal Cancer: Integrating Clinical, Treatment, and Radiomic Data to Predict Survival Outcomes with Interpretable Reasoning.

Bioengineering (Basel, Switzerland)·2025
Same author

Plasma pTau217 and Neurofilament Light Chain as Differentiative Markers for Progressive Supranuclear Palsy From Parkinson Disease.

Neurology·2025
Same author

Updates of nuclear medicine imaging in neurodegenerative disorders.

Journal of the Formosan Medical Association = Taiwan yi zhi·2025
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

We introduce E-COSEM, a novel algorithm for emission tomography reconstruction. This enhanced complete-data ordered subsets expectation-maximization method converges to the maximum likelihood solution, offering a fast and automatic alternative to existing techniques.

Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Computational Science

Background:

  • Ordered subsets expectation-maximization (OSEM) is widely used for emission tomography reconstruction.
  • OSEM is known for its speed but lacks guaranteed convergence to the maximum likelihood (ML) solution.
  • Existing alternatives like RAMLA, BSREM, and OS-SPS offer convergence but require manual parameter tuning.

Purpose of the Study:

  • To develop a novel, fast, and convergent algorithm for ML reconstruction in emission tomography.
  • To address the convergence limitations of OSEM without user-specified parameters.
  • To provide a robust alternative for accurate image reconstruction in emission imaging.

Main Methods:

  • Propose E-COSEM (enhanced complete-data ordered subsets expectation-maximization), an incremental EM algorithm.

Related Experiment Videos

  • Implement iteration-dependent parameters automatically computed to balance update speed and convergence.
  • Compare E-COSEM performance against OSEM, RAMLA, BSREM, and OS-SPS through simulations.

    • Main Results:

    • E-COSEM demonstrates convergence to the ML solution, unlike standard OSEM.
    • The algorithm automatically adjusts parameters, eliminating the need for user specification.
    • Simulations show E-COSEM achieves nearly the speed of RAMLA for ML reconstruction.

    Conclusions:

    • E-COSEM offers a fast, convergent, and user-friendly approach for ML reconstruction in emission tomography.
    • It provides a significant improvement over OSEM by ensuring convergence.
    • The automatic parameter computation makes E-COSEM a practical and efficient tool for medical imaging applications.