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

Positron Emission Tomography01:29

Positron Emission Tomography

8.1K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
8.1K

You might also read

Related Articles

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

Sort by
Same author

Detailed pharmacokinetic features of a novel bis-boron <sup>18</sup>F-trifluoroborate acid in healthy volunteers: comparable and additional values of total-body PET-imaging-derived analysis.

European journal of nuclear medicine and molecular imaging·2026
Same author

Outcome-driven dosimetry optimization for [<sup>177</sup>Lu]Lu-PSMA-617 radiopharmaceutical therapy: proof of concept on single time point dosimetry optimization.

European journal of nuclear medicine and molecular imaging·2026
Same author

Impact of scan duration and scatter correction on quantitative accuracy in Yttrium-90 PET/CT imaging using LAFOV and SAFOV systems: a phantom and clinical evaluation.

EJNMMI physics·2026
Same author

Visual analysis of [<sup>18</sup>F]FDG PET/CT imaging yields superior diagnostic accuracy compared to semiquantitative analysis in the assessment of giant cell arteritis.

European journal of nuclear medicine and molecular imaging·2026
Same author

Divergent scalp-to-region distance alteration patterns in autism spectrum disorders, Parkinson's disease and Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same author

German Society of Nuclear Medicine procedure guideline for brain perfusion SPECT using 99mTc-labelled radiopharmaceuticals (09/2025).

Nuklearmedizin. Nuclear medicine·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
Same journal

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026
Same journal

HyperCOCO: Multi-sensory HyperCOgnitive COmputing for learning population level brain connectivity.

Medical image analysis·2026
Same journal

PANTHER Challenge Report: Cross-Domain Pancreatic Tumor Segmentation in Magnetic Resonance Imaging.

Medical image analysis·2026
Same journal

DGCD-3D: Difference-guided conditional diffusion model for low-field 3D MRI enhancement to assist stroke assessment.

Medical image analysis·2026
Same journal

Leveraging modality-guided pre-training for dual-prompt-driven multi-cancer PET-CT segmentation.

Medical image analysis·2026
See all related articles
  1. Home
  3. Dose-aware Diffusion Model For 3d Pet Image Denoising: Multi-institutional Validation With Reader Study And Real Low-dose Data.
  1. Home
  3. Dose-aware Diffusion Model For 3d Pet Image Denoising: Multi-institutional Validation With Reader Study And Real Low-dose Data.

Related Experiment Video

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.3K

Dose-aware diffusion model for 3D PET image denoising: Multi-institutional validation with reader study and real

Huidong Xie1, Weijie Gan2, Reimund Bayerlein3

  • 1Department of Biomedical Engineering, Yale University, USA.

Medical Image Analysis
|April 3, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A novel dose-aware diffusion model, DDPET-3D, enhances low-dose PET imaging by improving image quality and consistency across diverse scanners and protocols. This deep learning approach offers comparable or superior results to full-dose scans, aiding in reduced radiation exposure.

Keywords:
Diffusion modelsLow-dose imagingPET denoising

More Related Videos

A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods
09:49

A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods

Published on: April 24, 2020

10.6K
Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
14:21

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

19.0K

Related Experiment Videos

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.3K
A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods
09:49

A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods

Published on: April 24, 2020

10.6K
Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
14:21

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

19.0K

Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Low-count/low-dose Positron Emission Tomography (PET) imaging requires reduced scan times and radiation dose while maintaining image quality, especially for lower-performance scanners.
  • Existing deep learning (DL) denoising models for PET often compromise image quality, lack generalizability across different noise levels, acquisition protocols, and patient populations.
  • While diffusion models show promise for high-quality sample generation in medical imaging, current applications in low-dose PET struggle with 3D reconstruction consistency, generalization, and accurate detail rendering.

Purpose of the Study:

  • To develop and evaluate DDPET-3D, a dose-aware diffusion model designed for 3D-consistent low-dose PET imaging.
  • To address limitations of existing diffusion models in generating consistent 3D reconstructions, generalizing across noise levels, and avoiding distorted details in low-dose PET.
  • To validate the model's performance using a large, multi-center dataset and assess its potential for clinical application.

    • Main Methods:

    • Development of DDPET-3D, a dose-aware diffusion model utilizing a 2.5D conditioning backbone for 3D-consistent reconstruction.
    • Extensive evaluation using 9783 18F-FDG PET studies from 4 global medical centers, covering low-dose levels from 1% to 50% and diverse scanners/protocols.
    • Cross-center and cross-scanner validation to assess model generalizability, supplemented by reader studies and Monte Carlo simulations for quantitative accuracy.

    Main Results:

    • DDPET-3D demonstrated strong generalizability across different low-dose levels, scanners, and clinical protocols, validated across multiple centers.
    • Nuclear medicine physicians rated denoised images from DDPET-3D as comparable or superior to full-dose images and existing DL baselines in qualitative assessments.
    • Quantitative evaluation using Monte Carlo simulations and a lesion segmentation network indicated accurate lesion-level quantitative accuracy, supporting the potential for low-dose PET with maintained image quality.

    Conclusions:

    • DDPET-3D effectively addresses challenges in 3D low-dose PET imaging, offering consistent and high-quality reconstructions.
    • The model shows significant potential for clinical translation, enabling reduced radiation dose in PET scans without compromising diagnostic image quality.
    • Public availability of code and models facilitates further research and application of advanced diffusion models in medical imaging.