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

A unified noise analysis for iterative image estimation.

Jinyi Qi1

  • 1Department of Nuclear Medicine and Functional Imaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. jqi@lbl.gov

Physics in Medicine and Biology
|December 5, 2003
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

Label-free fluorescence lifetime imaging for rapid discrimination of high-grade prostate cancer in fresh biopsy cores: a feasibility study.

Journal of biomedical optics·2026
Same author

Development and Initial Evaluation of 3D-printed High Resolution Brain Phantom for PET.

IEEE transactions on radiation and plasma medical sciences·2025
Same author

Developing Topics.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Exceptional brain PET images from the NeuroEXPLORER: scans with targeted radiopharmaceuticals and comparison to HRRT.

European journal of nuclear medicine and molecular imaging·2025
Same author

Bayesian additive regression trees for machine learning to classify benign vs atypical lipomatous tumors on MRI.

Radiology advances·2025
Same author

A Time-Walk and Timing-Shift Correction Method for Dual-Ended Readout TOF-DOI PET Detectors.

IEEE transactions on radiation and plasma medical sciences·2025
Same journal

PAC-Net: patch adaptive cut-off network with differentiable module-wise K-learning for robust and efficient medical image segmentation.

Physics in medicine and biology·2026
Same journal

Four-dimensional on-beam computed tomography reconstruction using projection-difference images.

Physics in medicine and biology·2026
Same journal

Higher-order synergy-based ranking in transcriptomic communities via latent factors and O-information.

Physics in medicine and biology·2026
Same journal

Calculating biological dose distributions in hadrontherapy using GATE: the BioDose actor.

Physics in medicine and biology·2026
Same journal

A 1.5 mm BGO PET detector with DOI measurement.

Physics in medicine and biology·2026
Same journal

Development and validation of XrayMC: a dedicated Monte Carlo tool for X-ray imaging and radiation protection.

Physics in medicine and biology·2026
See all related articles

This study introduces a new theoretical noise analysis for iterative image reconstruction in tomography. The method accurately estimates image uncertainty for various algorithms, improving quantitative accuracy in medical imaging.

Area of Science:

  • Medical Imaging
  • Computational Science

Background:

  • Iterative image estimation is crucial in emission tomography.
  • Accurate uncertainty estimation is vital for quantitative analysis.
  • Existing iteration-based noise analysis has limitations in algorithm applicability and convergence.

Purpose of the Study:

  • To present a novel theoretical noise analysis for iterative image reconstruction.
  • To develop a method applicable to a broad range of preconditioned gradient-type algorithms.
  • To ensure consistency between iteration-based and fixed-point noise analysis.

Main Methods:

  • Developed a theoretical noise analysis applicable to preconditioned gradient-type algorithms.
  • Showed consistency between iteration-based and fixed-point analysis by deriving fixed-point expressions.

Related Experiment Videos

  • Validated the method with examples in emission and transmission tomography.

    • Main Results:

    • The proposed noise analysis is applicable to a wide range of algorithms, including those without explicit multiplicative updates.
    • The method demonstrates consistency with fixed-point analysis under specific conditions.
    • Validation using Monte Carlo simulations confirms the accuracy of the results.

    Conclusions:

    • The presented noise analysis offers a more general and consistent approach to uncertainty estimation in iterative tomography.
    • This advancement is crucial for enhancing the reliability of quantitative applications in emission and transmission tomography.
    • The method provides a robust tool for validating image reconstruction algorithms.