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

Convergence study of an accelerated ML-EM algorithm using bigger step size.

DoSik Hwang1, Gengsheng L Zeng

  • 1Department of Bioengineering, University of Utah, Salt Lake City, UT 84112, USA. DoSik.Hwang@utah.edu

Physics in Medicine and Biology
|January 6, 2006
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

An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation.

Science advances·2026
Same author

Key Measures for Evaluating Diagnostic Accuracy in Multi-Class Classification: An Overview and Simulation-Based Comparison.

Korean journal of radiology·2026
Same author

Image Reconstruction with Maclaurin Series Expansion.

International journal of biomedical research & practice·2026
Same author

HP-GAN: Harnessing pretrained networks for GAN improvement with FakeTwins and discriminator consistency.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A Higher-Order Ising Model with Gradient-Free Update.

Axioms·2026
Same author

Limited-Angle Tomography Using a Neural Network as the Objective Function.

International journal of biomedical research & practice·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
Same journal

Technical Note: Real-Time MLC Control and Latency Measurement Optimization with External Verification.

Physics in medicine and biology·2026
Same journal

Fetus-Specific Hematopoietic Stem Cell Dosimetry Framework for Leukemia-Relevant Target Cells During Prenatal Development.

Physics in medicine and biology·2026
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
See all related articles

This study introduces an accelerated Maximum-Likelihood Expectation-Maximization (ML-EM) algorithm for SPECT/PET imaging. The new method improves convergence speed while maintaining image quality, offering advantages over existing techniques.

Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Computational Science

Background:

  • Maximum-Likelihood Expectation-Maximization (ML-EM) is crucial for accurate SPECT/PET reconstruction.
  • ML-EM incorporates physical factors for superior accuracy over analytical methods.
  • Slow convergence of ML-EM necessitates acceleration techniques.

Purpose of the Study:

  • To present an accelerated ML-EM algorithm with an increased step size.
  • To analyze the convergence properties of the accelerated ML-EM algorithm.
  • To compare the proposed method against other acceleration techniques.

Main Methods:

  • Development of an accelerated ML-EM algorithm.
  • Evaluation of convergence using variance noise and log-likelihood metrics.

Related Experiment Videos

  • Comparative analysis with existing accelerating methods, including additive forms.

    • Main Results:

    • The accelerated ML-EM algorithm demonstrates improved convergence rates.
    • The method maintains high image quality in terms of statistical noise.
    • Additive forms of the proposed method show advantages over other acceleration techniques.

    Conclusions:

    • The accelerated ML-EM algorithm offers a viable solution for faster, high-quality SPECT/PET image reconstruction.
    • This advancement addresses the primary limitation of slow convergence in traditional ML-EM.
    • The proposed method enhances the practical application of ML-EM in nuclear medicine imaging.