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

Obesity01:24

Obesity

552
The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
552

You might also read

Related Articles

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

Sort by
Same author

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

Medical image analysis·2026
Same author

Recent progress in the patterning of perovskite films for photodetector applications.

Light, science & applications·2025
Same author

Clinical features and prognosis analysis of patients with follicular lymphoma: a real-world study in China.

Annals of hematology·2025
Same author

Thorough Physiological Assessment in Non-Culprit Vessels of Patients with Acute Myocardial Infarction: Is It a Required Action?

Cardiovascular drugs and therapy·2025
Same author

Chrysin Attenuates Myocardial Cell Apoptosis in Mice.

Cardiovascular toxicology·2025
Same author

Motion Management in Positron Emission Tomography/Computed Tomography and Positron Emission Tomography/Magnetic Resonance.

PET clinics·2025

Related Experiment Video

Updated: Jul 27, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

PET Image Denoising using a Deep-Learning Method for Extremely Obese Patients.

Hui Liu1, Hamed Yousefi2, Niloufar Mirian2

  • 1Department of Engineering Physics, Tsinghua University, and Key Laboratory of Particle & Radiation Imaging, Ministry of Education (Tsinghua University), Beijing, China, on leave from the Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA.

IEEE Transactions on Radiation and Plasma Medical Sciences
|June 7, 2023
PubMed
Summary

Deep learning noise reduction using U-Net improves clinical PET scan image quality in extremely obese patients. This method matches noise levels to lean subjects, preserving fine structures for consistent imaging.

Keywords:
FDG PETdeep learningextremely obese patientnoise reduction

More Related Videos

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: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Related Experiment Videos

Last Updated: Jul 27, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
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: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Clinical Positron Emission Tomography (PET) imaging quality suffers from high noise in extremely obese patients.
  • This noise degradation impacts diagnostic accuracy and consistency.
  • Standard noise reduction techniques may compromise image resolution.

Purpose of the Study:

  • To reduce noise in PET images of extremely obese subjects to levels comparable to lean subjects.
  • To ensure consistent imaging quality across different patient body types.
  • To evaluate a deep learning-based noise reduction method for clinical PET scans.

Main Methods:

  • A fully 3D patch-based U-Net deep learning model was employed for noise reduction.
  • Two U-Nets (A and B) were trained on PET data from lean subjects at 40% and 10% count levels, respectively.
  • The trained U-Nets were applied to PET images of 10 extremely obese subjects, with noise assessed via liver Normalized Standard Deviation (NSTD).

Main Results:

  • U-Net A, trained on 40% count data, effectively reduced noise in obese patients' PET images (liver NSTD from 0.13±0.04 to 0.08±0.03, p=0.01).
  • Post-denoising, noise levels in obese subjects matched those of lean subjects (0.08±0.03 vs. 0.08±0.02, p=0.74) while preserving fine structures.
  • U-Net B (10% count data) resulted in over-smoothing and blurred image details.

Conclusions:

  • A U-Net trained on lean subject data with matched count levels offers effective noise reduction for extremely obese patients in clinical PET scans.
  • The method successfully maintained image resolution and consistency.
  • Further clinical validation is recommended to confirm the utility of this deep learning approach.