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

Computed Tomography01:10

Computed Tomography

7.9K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.9K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

711
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
711
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

213
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
213

You might also read

Related Articles

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

Sort by
Same author

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026
Same author

Predicting early-stage breast cancer disease-free survival and adjuvant therapy benefit from multimodal information using deep learning.

NPJ breast cancer·2026
Same author

Lumi-Guide: An Artificial-Intelligence-Driven Multimodal Framework for Optimizing Personalized Neoadjuvant Therapy Decision-Making in Luminal Breast Cancer.

Research (Washington, D.C.)·2026
Same author

Response to letter.

The British journal of radiology·2026
Same author

PIEZO1 is Required for Acute Myeloid Leukemia Progression and Leukemia Stem Cell Maintenance via HIF1A-SLC7A11 Axis-Mediated Ferroptosis Defense.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Tailoring the Extent of Lymphadenectomy for Esophageal Squamous Cell Carcinoma: Insights From a Comparative Study of Neoadjuvant Chemo-Immunotherapy and Surgery Cohort.

Thoracic cancer·2026

Related Experiment Video

Updated: Dec 25, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

932

Normalization of multicenter CT radiomics by a generative adversarial network method.

Yajun Li1,2, Guoqiang Han1,2, Xiaomei Wu1

  • 1South China University of Technology, Guangzhou 510006, People's Republic of China.

Physics in Medicine and Biology
|March 27, 2020
PubMed
Summary
This summary is machine-generated.

Generative adversarial networks (GANs) reduce computed tomography (CT) imaging protocol variability in radiomics features. This method enhances multicenter radiomics analysis by normalizing image data across different scanners and protocols.

Keywords:
CTGANmulticenter radiomicsnormalization

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.3K
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

1.6K

Related Experiment Videos

Last Updated: Dec 25, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

932
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.3K
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

1.6K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiomics

Background:

  • Radiomics analysis is sensitive to variations in computed tomography (CT) imaging protocols.
  • Inconsistent imaging protocols across different institutions lead to variability in radiomics features, hindering multicenter studies.

Purpose of the Study:

  • To develop and evaluate a generative adversarial network (GAN)-based method for normalizing CT images.
  • To reduce radiomics feature variability caused by different CT imaging protocols.
  • To facilitate robust multicenter radiomics analysis.

Main Methods:

  • Utilized a generative adversarial network (GAN) to normalize CT images from multiple domains (protocols/scanners) to a target domain.
  • Extracted 77 radiomics features and compared feature distributions before and after GAN-based normalization.
  • Assessed the model's performance using spleen and colorectal cancer datasets from different scanners.
  • Validated the method's utility in multicenter analysis by building and cross-validating a least absolute shrinkage and selection operator (LASSO) classifier.

Main Results:

  • Post-normalization, the alignment of radiomics features between different domains and the target domain significantly increased (e.g., from 10.4% to 93.5% for domain A vs. T).
  • The GAN-based normalization improved the average area under the receiver operating characteristic curve (AUC) by 11% (3%-32%) in cross-validation for distinguishing cancer survivors.
  • Demonstrated successful reduction in feature variability and enhanced performance in multicenter radiomics tasks.

Conclusions:

  • The proposed GAN-based normalization effectively reduces radiomics feature variability stemming from diverse CT imaging protocols.
  • This method shows significant potential for improving the reliability and applicability of multicenter radiomics studies.
  • Enables more consistent and accurate diagnostic and prognostic predictions across different clinical settings.