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 Experiment Video

Updated: Dec 25, 2025

Computer-Generated Animal Model Stimuli
26:43

Computer-Generated Animal Model Stimuli

Published on: July 29, 2007

11.3K

GAN-based synthetic brain PET image generation.

Jyoti Islam1, Yanqing Zhang2

  • 1Department of Computer Science, Georgia State University, Atlanta, Georgia, 30302-5060, USA. jislam2@student.gsu.edu.

Brain Informatics
|April 2, 2020
PubMed
Summary
This summary is machine-generated.

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

Opposing sensitive period effects of early and late childhood maltreatment on corticolimbic responses in fear conditioning.

Molecular psychiatry·2026
Same author

Nonlinear chiral magnetoconductivity induced by Zeeman field in magnetic Weyl semimetals.

Journal of physics. Condensed matter : an Institute of Physics journal·2026
Same author

Risk factors for early neurological deterioration in patients with acute ischaemic stroke and assessment of short-term prognosis.

Frontiers in neurology·2026
Same author

Realizing Electro-Optic Switching and Radiative Cooling in Smart Window Films via Fluorinated Monomer Doping.

ACS applied materials & interfaces·2026
Same author

Moderate thinning enhances soil water and its temporal stability in Chinese pine plantations on the semi-arid Loess Plateau of China.

Frontiers in plant science·2026
Same author

A rare initial presentation of giant pheochromocytoma: a case report of acute pulmonary embolism complicated by catecholamine-induced cardiomyopathy.

Frontiers in oncology·2026
Same journal

Parkinson's disease classification using optimized attention-based deep learning from EEG signals with interpretable sub-band topography.

Brain informatics·2026
Same journal

A quantitative and precision‑oriented neuronal reconstruction approach based on data grading.

Brain informatics·2026
Same journal

Evaluating multi-level membership inference risk in federated EEG learning.

Brain informatics·2026
Same journal

Single-cell reconstruction of whole-brain efferent projections from mouse ventral posteromedial thalamus.

Brain informatics·2026
Same journal

RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.

Brain informatics·2026
Same journal

Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions.

Brain informatics·2026
See all related articles

This study introduces a new method using generative adversarial networks (GANs) to create synthetic brain PET images for Alzheimer's disease diagnosis. This approach addresses the challenge of limited medical data for training AI models.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Deep learning excels in computer vision but requires large annotated datasets, which are difficult to obtain for medical imaging.
  • Limited medical datasets hinder the development of robust automated disease diagnosis models.
  • Analyzing brain positron emission tomography (PET) scans is crucial for diagnosing neurodegenerative diseases like Alzheimer's.

Purpose of the Study:

  • To develop a novel approach for generating synthetic medical images using generative adversarial networks (GANs).
  • To create realistic brain PET images representing different stages of Alzheimer's disease: normal control (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD).
  • To overcome data scarcity challenges in medical image analysis for AI-driven diagnostics.
Keywords:
Alzheimer’s diseaseBrain imagingGenerative adversarial networksPositron emission tomography (PET)Synthetic medical image generation

More Related Videos

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.1K
Author Spotlight: Simple and Efficient Neural Retina Organoid Production for Disease Modeling
05:03

Author Spotlight: Simple and Efficient Neural Retina Organoid Production for Disease Modeling

Published on: December 22, 2023

1.7K

Related Experiment Videos

Last Updated: Dec 25, 2025

Computer-Generated Animal Model Stimuli
26:43

Computer-Generated Animal Model Stimuli

Published on: July 29, 2007

11.3K
Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform
07:57

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

3.1K
Author Spotlight: Simple and Efficient Neural Retina Organoid Production for Disease Modeling
05:03

Author Spotlight: Simple and Efficient Neural Retina Organoid Production for Disease Modeling

Published on: December 22, 2023

1.7K

Main Methods:

  • Implementation of a generative adversarial network (GAN) architecture.
  • Training the GAN on existing brain PET datasets.
  • Utilizing the trained GAN to synthesize new brain PET images for NC, MCI, and AD stages.

Main Results:

  • Successful generation of synthetic brain PET images.
  • Generated images represent distinct stages of Alzheimer's disease (NC, MCI, AD).
  • The synthetic data can potentially augment limited real-world medical datasets.

Conclusions:

  • Generative adversarial networks offer a promising solution for creating synthetic medical imaging data.
  • The proposed GAN model can generate diverse brain PET images for Alzheimer's disease research.
  • This technique can aid in developing more robust automated diagnostic tools by addressing data limitations.