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 Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Long-Time Storage of a Qubit Encoded in Decoherence-Free Subspace Using a Dual-Type Quantum Memory.

Physical review letters·2025
Same author

Retraction Note: Effect of miR-363 on the proliferation, invasion and apoptosis of laryngeal cancer by targeting Mcl-1.

European review for medical and pharmacological sciences·2025
Same author

Correction: LincRNA-ROR induces epithelial-to-mesenchymal transition and contributes to breast cancer tumorigenesis and metastasis.

Cell death & disease·2025
Same author

A site-resolved two-dimensional quantum simulator with hundreds of trapped ions.

Nature·2024
Same author

[Effectiveness of comprehensive echinococcosis control measures with emphasis on management of infectious source in Sichuan Province from 2010 to 2022].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control·2024
Same author

[One case of acute severe nitrite poisoning with massive pulmonary thromboembolism].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2023

Related Experiment Video

Updated: Apr 19, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

911

Applications of the line-of-response probability density function resolution model in PET list mode reconstruction.

Y Jian1, R Yao, T Mulnix

  • 1Department of Biomedical Engineering, Yale University, New Haven, CT, USA.

Physics in Medicine and Biology
|December 10, 2014
PubMed
Summary

The novel Line of Response probability density function (LOR-PDF) model improves Positron Emission Tomography (PET) image resolution and contrast. This spatially variant method enhances image quality in clinical PET scanners like the HRRT and Focus-220.

More Related Videos

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.8K
Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

717

Related Experiment Videos

Last Updated: Apr 19, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

911
Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.8K
Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

717

Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Image Reconstruction

Background:

  • Positron Emission Tomography (PET) image quality is limited by resolution degradation.
  • Accurate modeling of physical acquisition factors is crucial for effective resolution modeling (RM).
  • Previous work introduced a probability density function (PDF) method for deriving resolution kernels and parameterizing lines of response (LORs).

Purpose of the Study:

  • To evaluate the performance of the novel Line of Response probability density function (LOR-PDF) model.
  • To assess the LOR-PDF model's impact on image resolution and contrast in two PET scanners (HRRT and Focus-220).
  • To demonstrate the advantages of crystal-layer-dependent resolution modeling.

Main Methods:

  • Implemented and tested the LOR-PDF model on HRRT and Focus-220 PET scanners.
  • Replaced spatially invariant kernels with spatially variant LOR-PDF for image reconstruction.
  • Evaluated resolution using point source reconstructions and contrast using phantom studies.
  • Validated findings with in-vivo rat studies using [(11)C]AFM and [(11)C]PHNO.

Main Results:

  • LOR-PDF achieved a more uniform resolution distribution across the field of view.
  • Measured in-plane Full Width at Half Maximum (FWHM) for point sources on the HRRT ranged from 1.7 mm to 1.9 mm.
  • LOR-PDF yielded up to 9% higher contrast at equivalent noise levels compared to image-space RM.
  • Statistically verified contrast improvement and observed higher contrast in small, high-uptake regions in rat studies.

Conclusions:

  • The LOR-PDF model significantly improves PET image resolution and contrast.
  • Spatially variant resolution modeling with LOR-PDF enhances image uniformity and diagnostic accuracy.
  • LOR-PDF offers superior performance for crystal-layer-dependent modeling and small lesion detection in PET imaging.