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

4.2K
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...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Generative Consistency Models for Estimation of Kinetic Parametric Image Posteriors in Total-Body PET.

IEEE transactions on medical imaging·2026
Same author

Diffusion models for medical image reconstruction.

BJR artificial intelligence·2026
Same author

Targeting TPX2-dependent lineage plasticity by CDK4/6 inhibition reverses therapy resistance in neuroendocrine bladder carcinoma.

Cell reports. Medicine·2026
Same author

Miniature and versatile genome regulation TnpB-ωRNA toolkits facilitate cancer immunotherapy.

Nature communications·2026
Same author

Personalized MR-Informed Diffusion Models for 3D PET Image Reconstruction.

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

Reprogramming the tumor microenvironment with c-MYC-based gene circuit platform to enhance specific cancer immunotherapy.

Nature communications·2025
Same journal

It's PET but not as we know it: radiation protection considerations when using a novel specimen PET-CT scanner during tumour resections for urology and head & neck patients.

EJNMMI physics·2026
Same journal

Data-efficient unsupervised deep learning deformable SPECT/CT registration framework for voxel-level radionuclide therapy dosimetry: validation using clinical <sup>131</sup>I DTC therapy data.

EJNMMI physics·2026
Same journal

Impact of reduced <sup>18</sup>F-MK6240 PET/MR acquisition duration on image quality and tau pathology assessment in patients with cognitive impairment.

EJNMMI physics·2026
Same journal

Optimising [Formula: see text] PET imaging for dosimetry in SIRT: insights from phantom and simulation studies on the Discovery MI scanner.

EJNMMI physics·2026
Same journal

Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).

EJNMMI physics·2026
Same journal

Limitations in diagnostics and quantification of small lesions with low uptake in the clinical context of prostate <sup>18</sup>F/<sup>68</sup>Ga-PSMA PET/MRI.

EJNMMI physics·2026
See all related articles

Related Experiment Video

Updated: Jun 22, 2025

Simultaneous PET/MRI Imaging During Mouse Cerebral Hypoxia-ischemia
10:35

Simultaneous PET/MRI Imaging During Mouse Cerebral Hypoxia-ischemia

Published on: September 20, 2015

12.3K

Kinetic model-informed deep learning for multiplexed PET image separation.

Bolin Pan1, Paul K Marsden2, Andrew J Reader2

  • 1School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. bolin.pan@kcl.ac.uk.

EJNMMI Physics
|July 1, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method for multiplexed positron emission tomography (mPET) image separation. The kinetic model-informed approach improves accuracy and requires fewer training examples for clearer single-tracer PET imaging.

Keywords:
Kinetic modelingMultiplexed PETPhysics-informed deep learningSpectral analysis

More Related Videos

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

18.3K
High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
11:09

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

Published on: December 16, 2022

3.7K

Related Experiment Videos

Last Updated: Jun 22, 2025

Simultaneous PET/MRI Imaging During Mouse Cerebral Hypoxia-ischemia
10:35

Simultaneous PET/MRI Imaging During Mouse Cerebral Hypoxia-ischemia

Published on: September 20, 2015

12.3K
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

18.3K
High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
11:09

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

Published on: December 16, 2022

3.7K

Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Artificial Intelligence

Background:

  • Multiplexed positron emission tomography (mPET) enables simultaneous measurement of multiple tracers in a single scan.
  • Separating signals from different tracers in mPET is challenging due to indistinguishable photon pairs.
  • Current methods lack unique energy information for differentiating tracer sources.

Purpose of the Study:

  • To develop an improved deep learning method for mPET image separation.
  • To enhance the inductive prior of deep networks by incorporating a kinetic model.
  • To achieve accurate separation of dual-tracer PET images.

Main Methods:

  • Incorporated a general kinetic model based on spectral analysis into a deep network.
  • Integrated the model and deep network into an unrolled image-space iterative 4D PET reconstruction algorithm.
  • Evaluated the method on simulated dual-tracer [18F]FDG+[11C]MET brain PET data.

Main Results:

  • The proposed method achieved separation performance comparable to single-tracer imaging.
  • Outperformed conventional model-based methods (v-MTCM, IS-F4D) and pure data-driven methods (CED).
  • Demonstrated superior performance with fewer training examples compared to CED.

Conclusions:

  • A kinetic model-informed unrolled deep learning method was proposed for mPET image separation.
  • The method shows significant advantages over existing techniques in simulation studies.
  • This approach offers a promising direction for advancing mPET image analysis.