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

Computed Tomography01:10

Computed Tomography

4.5K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
4.5K

You might also read

Related Articles

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

Sort by
Same author

Lung motion estimation from 4D CT using structure-tensor-guided finite-element digital volume correlation.

Physics in medicine and biology·2026
Same author

The impact of scan time on dynamic [Formula: see text]-FAPI-04 total-body PET parametric imaging generated by deep learning models.

EJNMMI physics·2026
Same author

A network analysis of whole-body [<sup>18</sup>F]FDG PET/CT reveals glycaemia-associated reorganization of systemic metabolic coordination.

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

Association of functional brain alterations with β-amyloid, tau, and cognitive decline in Alzheimer's disease.

Alzheimer's research & therapy·2026
Same author

Whole-body <sup>18</sup>F-FDG PET/CT identifies subclinical metabolic phenotypes in normoglycemic adults.

European journal of nuclear medicine and molecular imaging·2025
Same author

Mapping systemic inter-organ metabolic networks across glycemic continuum using whole-body [<sup>18</sup>F]FDG PET/CT and machine learning.

European journal of nuclear medicine and molecular imaging·2025

Related Experiment Video

Updated: Jul 3, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

Learning CT-free attenuation-corrected total-body PET images through deep learning.

Wenbo Li1,2, Zhenxing Huang1, Zixiang Chen1,2

  • 1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.

European Radiology
|February 15, 2024
PubMed
Summary

This study introduces a deep learning method to create CT-free attenuation-corrected total-body PET images, significantly reducing patient radiation exposure. The generated images maintain high quality, offering a safer alternative for PET/CT imaging, especially for vulnerable patient groups.

Keywords:
Deep learningPositron emission tomographyRadiation

More Related Videos

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis
07:45

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Published on: October 25, 2024

378
Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
05:32

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

278

Related Experiment Videos

Last Updated: Jul 3, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis
07:45

Author Spotlight: Standardizing Mouse In Vivo PET Imaging with Body Conforming Molds and Automated Analysis

Published on: October 25, 2024

378
Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
05:32

Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph

Published on: February 21, 2025

278

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Total-body PET/CT scanners offer improved image quality but expose patients to ionizing radiation from CT scans.
  • Reducing radiopharmaceutical doses in total-body PET/CT exacerbates the radiation burden from CT.
  • Current methods necessitate CT for attenuation correction, posing a radiation risk.

Purpose of the Study:

  • To develop a deep learning-based method for generating CT-free attenuation-corrected (CTF-AC) total-body PET images.
  • To reduce the ionizing radiation dose to patients undergoing total-body PET/CT scans.
  • To provide a viable alternative for PET/CT imaging, particularly for pediatric and chronically ill patients.

Main Methods:

  • Utilized a cycle-consistent generative adversarial network (Cycle-GAN) on total-body PET data from 122 subjects.
  • Incorporated site structures as prior information to generate CTF-AC images.
  • Performed statistical analyses, including Pearson correlation coefficient and t-tests, for validation.

Main Results:

  • Generated CTF-AC total-body PET images closely resembled real attenuation-corrected PET images.
  • Achieved high peak signal-to-noise ratio (36.92 ± 5.49 dB) and structural similarity index measure (0.980 ± 0.041).
  • Demonstrated consistent standardized uptake value (SUV) distribution compared to real AC PET images.

Conclusions:

  • The deep learning approach successfully generates CTF-AC total-body PET images, reducing radiation risk.
  • The method is validated across different radiopharmaceutical doses and shows potential for low-dose PET attenuation correction.
  • This CT-free method is beneficial for PET/MRI or PET-only systems and reduces radiation from redundant anatomical scans.