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

Tracer-agnostic diffusion model-based CT-free attenuation correction for brain PET: Comprehensive evaluation across 14 tracers.

Physics in medicine and biology·2026
Same author

Feasibility study of image reconstruction for a forceps-type positron emission counter: a simulation-based algorithm comparison.

Physics in medicine and biology·2026
Same author

Denoising of ultra-low-dose <sup>15</sup>O positron emission tomography images using deep image prior with anatomical information extracted through magnetic resonance segmentation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2026
Same author

Visualization of nonlinearity in image reconstruction using nonlocal means filters for regularization under noisy conditions.

Radiological physics and technology·2026
Same author

Sub-0.5-mm Resolution PET Versus Autoradiography: Comparison of mGluR1 Concentrations in Mouse Brain.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same author

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

IEEE transactions on medical imaging·2026

Related Experiment Video

Updated: Jul 4, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

Deep learning-based PET image denoising and reconstruction: a review.

Fumio Hashimoto1,2,3, Yuya Onishi4, Kibo Ote4

  • 1Central Research Laboratory, Hamamatsu Photonics K. K, 5000 Hirakuchi, Hamana-Ku, Hamamatsu, 434-8601, Japan. fumio.hashimoto@crl.hpk.co.jp.

Radiological Physics and Technology
|February 6, 2024
PubMed
Summary

This review details the evolution of positron emission tomography (PET) image reconstruction, from traditional methods to advanced deep learning techniques for improved PET imaging. It covers denoising, end-to-end reconstruction, and hybrid iterative approaches, highlighting future directions.

Keywords:
Convolutional neural networksDeep learningImage reconstructionPositron emission tomography

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

Related Experiment Videos

Last Updated: Jul 4, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

Area of Science:

  • Medical Imaging
  • Computer Science
  • Artificial Intelligence

Background:

  • Positron Emission Tomography (PET) is a crucial molecular imaging technique.
  • Accurate image reconstruction is vital for diagnostic efficacy in PET.
  • Conventional reconstruction algorithms have limitations in speed and resolution.

Purpose of the Study:

  • To provide a comprehensive overview of PET image reconstruction algorithms.
  • To review the integration and impact of deep learning in PET imaging.
  • To explore future trends at the intersection of PET and AI.

Main Methods:

  • Overview of conventional PET reconstruction: Filtered Backprojection (FBP) and iterative algorithms.
  • Review of deep learning (DL) approaches in PET: post-processing, direct reconstruction, and hybrid iterative methods.
  • Analysis of DL techniques applied to PET data for enhanced image quality and reconstruction.

Main Results:

  • Deep learning methods offer significant improvements in PET image denoising and reconstruction.
  • End-to-end DL models learn direct sinogram-to-image mappings effectively.
  • Hybrid iterative methods combine traditional algorithms with neural networks for superior performance.

Conclusions:

  • Deep learning is revolutionizing PET image reconstruction, offering enhanced accuracy and efficiency.
  • Future PET imaging will likely integrate advanced AI and machine learning techniques.
  • Continued research in DL for PET promises further breakthroughs in molecular imaging.