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

4.1K
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...
4.1K

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

Ultra-dense lutetium oxide ceramic scintillators for positron emission tomography.

Physics in medicine and biology·2026
Same journal

Optimization of entrance skin dose assessment: integration of a new mathematical model and Monte Carlo simulation.

Radiological physics and technology·2026
Same journal

Methodology for intrafraction organ motion tracking using ultrasound for respiratory-gated radiotherapy.

Radiological physics and technology·2026
Same journal

In vivo dosimetry of cardiac implantable electronic devices with a radiophotoluminescent glass dosimeter in patients undergoing radiotherapy.

Radiological physics and technology·2026
Same journal

Calibration of diffusion MRI measurements using aqueous glycerin phantoms with controlled viscosity.

Radiological physics and technology·2026
Same journal

Establishing discard criteria for lead aprons using deep learning-based quantification of defect area on X-ray fluoroscopic video.

Radiological physics and technology·2026
Same journal

A reproducible framework for constructing a longitudinal low-dose CT screening image database: implementation using 11 years of real-world data.

Radiological physics and technology·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2025

Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals
08:02

Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals

Published on: November 15, 2024

566

Two-step optimization for accelerating deep image prior-based PET image reconstruction.

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
|August 3, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a faster two-step method for deep image prior (DIP) based positron emission tomography (PET) image reconstruction, significantly improving efficiency and image quality for clinical applications.

Keywords:
3D PET image reconstructionDeep image priorEnd-to-end reconstructionPositron emission tomography (PET)

More Related Videos

Radiosynthesis, Quality Control, and Small Animal Positron Emission Tomography Imaging of 68Ga-Labelled Nano Molecules
09:55

Radiosynthesis, Quality Control, and Small Animal Positron Emission Tomography Imaging of 68Ga-Labelled Nano Molecules

Published on: October 4, 2024

339
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 18, 2025

Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals
08:02

Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals

Published on: November 15, 2024

566
Radiosynthesis, Quality Control, and Small Animal Positron Emission Tomography Imaging of 68Ga-Labelled Nano Molecules
09:55

Radiosynthesis, Quality Control, and Small Animal Positron Emission Tomography Imaging of 68Ga-Labelled Nano Molecules

Published on: October 4, 2024

339
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
  • Artificial Intelligence
  • Computational Science

Background:

  • Deep learning, especially CNNs, has improved PET image reconstruction but demands large, high-quality datasets.
  • Unsupervised methods like Deep Image Prior (DIP) show potential but are computationally intensive.
  • Existing DIP-based PET reconstruction methods are effective but time-consuming.

Purpose of the Study:

  • To accelerate end-to-end Deep Image Prior (DIP)-based positron emission tomography (PET) image reconstruction.
  • To enhance the image quality of PET scans reconstructed using DIP methods.
  • To develop a practical and efficient approach for DIP-based PET image reconstruction.

Main Methods:

  • Proposed a novel two-step optimization strategy for DIP-based PET image reconstruction.
  • Implemented a pre-training phase utilizing conditional DIP denoising.
  • Incorporated an end-to-end reconstruction phase with fine-tuning.

Main Results:

  • The two-step method significantly reduced computation time compared to standard DIP methods.
  • Evaluations demonstrated a notable improvement in PET image quality.
  • Monte Carlo simulations validated the efficiency and effectiveness of the proposed approach.

Conclusions:

  • The proposed two-step optimization method offers a practical and efficient solution for accelerating DIP-based PET image reconstruction.
  • This approach enhances PET image quality while reducing computational demands.
  • It represents a significant advancement for applying unsupervised learning in medical imaging.