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

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin·2026
Same author

Deep-Learning-Based Image Reconstruction to Improve End-Diastolic and Systolic Cardiac T1 Mapping.

Magnetic resonance in medicine·2026
Same author

Automated Coregistered Segmentation for Volumetric Analysis of Multiparametric Renal MRI.

Magnetic resonance in medicine·2026
Same author

Age- and BMI-Dependent Psoas and Gluteus Muscle Mass in 27,805 Participants of the Population-Based German National Cohort (NAKO Gesundheitsstudie): A Deep-Learning 3T MRI Study.

Diagnostics (Basel, Switzerland)·2026
Same author

Arterial Spin Labeling MRI for Noninvasive Monitoring of Treatment Response in Acute Pulmonary Embolism.

Radiology. Cardiothoracic imaging·2025
Same author

Multi-Frame Image Registration for Automated Ventricular Function Assessment in Single Breath-Hold Cine MRI Using Limited Labels.

Magnetic resonance in medicine·2025

Related Experiment Video

Updated: Jul 14, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities.

Thomas Küstner1, Tobias Hepp1, Ferdinand Seith2

  • 1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany.

Nuklearmedizin. Nuclear Medicine
|October 6, 2023
PubMed
Summary

Machine learning (ML) offers a viable clinical solution for processing and analyzing hybrid imaging data from MRI, CT, and PET scans. This technology promises to enhance image acquisition, quality, and decision-making in healthcare.

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.2K

Related Experiment Videos

Last Updated: Jul 14, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.2K

Area of Science:

  • Medical Imaging
  • Machine Learning Applications
  • Radiology and Nuclear Medicine

Background:

  • Machine learning (ML) is a pivotal technology for advancing healthcare data analysis.
  • Diagnostic radiology and nuclear medicine are poised for significant advancements through ML integration.

Conclusions:

  • ML presents a viable clinical solution for hybrid imaging (MRI, CT, PET) reconstruction, processing, and analysis.
  • ML has the potential to become an indispensable diagnostic and clinical tool.
  • Future work should address challenges to fully integrate ML into clinical practice.