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 methods for randoms-precorrected PET scans.

M Yavuz1, J A Fessler

  • 1Department of EECS, University of Michigan, Ann Arbor 48109-2122, USA.

Medical Image Analysis
|March 11, 1999
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

Electronic field effect detection of SARS-CoV-2 N-protein before the onset of symptoms.

Biosensors & bioelectronics·2022
Same author

An ultrasensitive heart-failure BNP biosensor using B/N co-doped graphene oxide gel FET.

Biosensors & bioelectronics·2021
Same author

Encapsulating Peritoneal Sclerosis in Peritoneal Dialysis Patients After Kidney Transplantation.

Transplantation proceedings·2018
Same author

Brachial artery protected by wrapped latissimus dorsi muscle flap in high voltage electrical injury.

Annals of burns and fire disasters·2017
Same author

Salvage of a free osteocutaneous fibula flap by creating a distal arteriovenous fistula in facial reconstruction.

International journal of oral and maxillofacial surgery·2015
Same author

An unusual cause of hoarseness: hydatid cyst of the thyroid.

Tropical biomedicine·2014
Same journal

Wavelet-inspired diffusion model with near-field constraint for real-time echocardiography dehazing.

Medical image analysis·2026
Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026
Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026
Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026
Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026
Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026
See all related articles

New positron emission tomography (PET) methods improve image reconstruction by using more accurate statistical models for accidental coincidences. These approaches reduce bias and variance compared to conventional techniques, enhancing PET imaging quality.

Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Physics

Background:

  • Positron emission tomography (PET) measurements require correction for accidental coincidence events.
  • Current randoms subtraction methods, while compensating for coincidences, disrupt Poisson statistics.
  • This leads to potential biases and reduced accuracy in image reconstruction.

Purpose of the Study:

  • To develop and analyze novel statistical models for PET precorrected measurements.
  • To improve the accuracy of log-likelihood approximations in PET image reconstruction.
  • To reduce systematic bias and variance in PET transmission tomography.

Main Methods:

  • Proposed two new approximations: a 'shifted Poisson' model and saddle-point approximations to the probability mass function (PMF).

Related Experiment Videos

  • Focused analysis on transmission tomography applications.
  • Compared new models against conventional data-weighted least-squares (WLS) and ordinary Poisson (OP) maximum-likelihood methods using simulations and analytic approximations.

    • Main Results:

    • The proposed methods effectively avoid the systematic bias inherent in WLS.
    • Significantly lower variance was observed compared to the conventional OP method.
    • The saddle-point method offered a more accurate log-likelihood approximation, while the shifted Poisson method showed comparable bias-variance performance in simulations.

    Conclusions:

    • New statistical modeling approaches offer improved image reconstruction in PET.
    • The shifted Poisson and saddle-point methods provide more realistic statistical modeling than conventional techniques.
    • These advanced methods enhance PET image quality with minimal increase in computational cost.