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

Maturational delay and asymmetric information flow of brain connectivity in SHR model of ADHD revealed by topological analysis of metabolic networks.

Scientific reports·2020
Same author

Clinical outcome and predictive factors for docetaxel and epirubicin neoadjuvant chemotherapy of locally advanced breast cancer.

The Korean journal of internal medicine·2020
Same author

Hypofractionated Radiation Therapy for Progressive Heterotopic Ossification: The Relationship between Dose and Efficacy.

International journal of radiation oncology, biology, physics·2020
Same author

Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports.

European journal of nuclear medicine and molecular imaging·2020
Same author

Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease.

European journal of nuclear medicine and molecular imaging·2019
Same author

Clinical implication of 18F-NaF PET/computed tomography indexes of aortic calcification in coronary artery disease patients: correlations with cardiovascular risk factors.

Nuclear medicine communications·2019

Related Experiment Video

Updated: Jan 3, 2026

Quantitative 3D In Silico Modeling q3DISM of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease
09:33

Quantitative 3D In Silico Modeling q3DISM of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease

Published on: December 26, 2016

8.3K

Amyloid PET Quantification Via End-to-End Training of a Deep Learning.

Ji-Young Kim1, Hoon Young Suh1, Hyun Gee Ryoo1

  • 1Department of Nuclear Medicine, Seoul National University Hospital, 010 Daehak-Ro Jongno-Gu, Seoul, 03080 South Korea.

Nuclear Medicine and Molecular Imaging
|November 15, 2019
PubMed
Summary

A novel deep learning model simplifies amyloid positron emission tomography (PET) quantification, improving accuracy for Alzheimer's disease diagnosis. This one-step method is reliable across different centers and radiotracers.

Keywords:
Alzheimer’s diseaseAmyloid PETConvolutional neural networkDeep learningQuantification

More Related Videos

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

1.6K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.4K

Related Experiment Videos

Last Updated: Jan 3, 2026

Quantitative 3D In Silico Modeling q3DISM of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease
09:33

Quantitative 3D In Silico Modeling q3DISM of Cerebral Amyloid-beta Phagocytosis in Rodent Models of Alzheimer's Disease

Published on: December 26, 2016

8.3K
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

1.6K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.4K

Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Amyloid PET imaging is crucial for evaluating cognitive impairment.
  • Current quantification methods are complex and require MRI, limiting clinical use.

Purpose of the Study:

  • To develop a simplified, one-step quantification method for amyloid PET using deep learning.
  • To enable routine clinical application of amyloid PET quantification regardless of radiotracer or imaging center.

Main Methods:

  • Utilized native-space amyloid PET images from the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset.
  • Trained and validated a deep learning model on 850 florbetapir PET images.
  • Tested the model on separate sets of florbetapir (366 images) and florbetaben (89 images) PET scans.

Main Results:

  • Achieved low mean absolute errors (MAEs) for standardized uptake value ratios (SUVRs): 0.040 (training/validation), 0.060 (florbetapir test), and 0.050 (florbetaben test).
  • Demonstrated high agreement in amyloid positivity classification (Cohen's kappa): 0.87 for florbetapir and 0.89 for florbetaben.

Conclusions:

  • A one-step deep learning quantification method for amyloid PET has been developed.
  • The model shows high reliability for quantifying amyloid PET across multicenter datasets and various radiotracers.