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

Dosimetric characterization of CdZnTe radiation detectors under electron-beam irradiation.

PloS one·2026
Same author

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same author

Linking kidney impairment to amygdala volume changes and mental disorders: a Mendelian randomization study.

Kidney research and clinical practice·2026
Same author

Detecting Secondary Medication Infusion Errors via Spectrophotometry.

Biomedical instrumentation & technology·2026
Same author

A Dual-Imaging Fluorescent and Iodinated Thermosensitive Hydrogel for Image-Guided Surgery of Pulmonary Nodules.

ACS applied bio materials·2026
Same author

<i>Operando</i> neutron radiography validates a parameter-free transport-kinetics model for thick solid-state battery cathodes.

Materials horizons·2026

Related Experiment Video

Updated: Jul 23, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

7.4K

Improvement of phoswich detector-based β+/γ-ray discrimination algorithm with deep learning.

Chanho Kim1, Semin Kim2, Yeeun Lee2

  • 1Korea Atomic Energy Research Institute (KAERI), Daejeon, South Korea.

Medical Physics
|July 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an Autoencoder-based algorithm to improve positron detection for cancer imaging. The new method enhances sensitivity and reduces errors in distinguishing true positrons from gamma rays, leading to more accurate tumor localization.

Keywords:
Autoencoderdeep learningphoswich detectorpositron detectionpulse shape discrimination technique

More Related Videos

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
06:28

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera

Published on: January 30, 2020

12.6K
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

Related Experiment Videos

Last Updated: Jul 23, 2025

Picometer-Precision Atomic Position Tracking through Electron Microscopy
15:04

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

7.4K
Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera
06:28

Visualization of Low-Level Gamma Radiation Sources Using a Low-Cost, High-Sensitivity, Omnidirectional Compton Camera

Published on: January 30, 2020

12.6K
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

Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Machine Learning in Healthcare

Background:

  • Positron probes aid malignant tumor localization using radiopharmaceuticals.
  • Conventional methods struggle to distinguish gamma rays from positrons, increasing detection errors.
  • Existing techniques like multilayer scintillator detection have limitations in accuracy.

Purpose of the Study:

  • To enhance positron detection accuracy by analyzing energy distribution in multilayer scintillator detectors.
  • To develop an improved algorithm for discriminating true positrons from false ones.
  • To reduce misidentification of gamma rays as positrons in tumor imaging.

Main Methods:

  • Utilized Autoencoder, an unsupervised deep learning model, for signal processing.
  • Trained Autoencoder to separate signals from each scintillator layer.
  • Applied energy windowing to energy distribution data for positron discrimination.

Main Results:

  • The Autoencoder-generated energy distribution map closely matched simulation results.
  • Positron detection sensitivity increased by 29.79% compared to conventional methods at an equal error rate.
  • The proposed method achieved a 25.0% lower error rate at the same sensitivity level.

Conclusions:

  • Developed an Autoencoder-based algorithm for superior true positron discrimination.
  • Demonstrated increased positron detection sensitivity with a maintained low error rate.
  • Potential for improved accuracy and speed in cancer localization using advanced probes and cameras.