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

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

45
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...
45
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

429
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...
429
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

133
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
133
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

50
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
50

You might also read

Related Articles

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

Sort by
Same author

Standardized Reporting of Cardiac Magnetic Resonance Examinations in Children With Cardiac Diseases and Adults With Congenital Heart Disease: A Scientific Statement From the Association for European Pediatric and Congenital Cardiology (AEPC) and the International Society for Magnetic Resonance in Medicine (ISMRM).

Journal of magnetic resonance imaging : JMRI·2026
Same author

Fibronectin-induced overactivation of α<sub>V</sub>β<sub>3</sub>-PI3K-PIP3-PDK1-ILK signaling drives aortic disease in Marfan syndrome.

Nature communications·2026
Same author

Beyond sudden cardiac death: the left atrium as a window into disease progression in hypertrophic cardiomyopathy.

European heart journal. Cardiovascular Imaging·2026
Same author

EACVI survey on the use of multi-modality cardiovascular imaging in immune-mediated inflammatory diseases.

European heart journal. Imaging methods and practice·2026
Same author

Corrigendum to: ApoB100 remodeling and stiffened cholesteryl ester core raise LDL aggregation in familial hypercholesterolemia patients [Journal of Lipid Research 66/1 (2025) 100703].

Journal of lipid research·2026
Same author

Design of an Interoperability Architecture for STAGE Person-Centred Applications for Clinicians and Ageing Citizens.

Studies in health technology and informatics·2026

Related Experiment Video

Updated: Sep 3, 2025

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

1.3K

Minimising multi-centre radiomics variability through image normalisation: a pilot study.

Víctor M Campello1, Carlos Martín-Isla2, Cristian Izquierdo2

  • 1Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain. victor.campello@ub.edu.

Scientific Reports
|July 22, 2022
PubMed
Summary
This summary is machine-generated.

Radiomics analysis in cardiovascular magnetic resonance imaging faces challenges with multi-center data variability. Normalization techniques like piecewise linear histogram matching improve classification generalization, outperforming unnormalized data.

More Related Videos

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K
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

673

Related Experiment Videos

Last Updated: Sep 3, 2025

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

1.3K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K
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

673

Area of Science:

  • Cardiovascular Imaging
  • Radiomics
  • Biomedical Data Science

Background:

  • Radiomics offers advanced cardiovascular disease phenotyping but is often limited to single-center studies.
  • Multi-center studies face challenges due to inherent image variability across different institutions.
  • Standardizing radiomics data is crucial for reliable multi-center research.

Purpose of the Study:

  • To comprehensively analyze radiomics variability across different normalization techniques in a multi-center cardiovascular magnetic resonance dataset.
  • To evaluate the impact of normalization on feature distributions and classification performance.
  • To assess the generalizability of radiomics features for hypertrophic cardiomyopathy classification.

Main Methods:

  • Utilized a multi-center cardiovascular magnetic resonance dataset with 218 subjects (112 healthy, 106 hypertrophic cardiomyopathy) from five centers.
  • Extracted first and second-order texture radiomic features from ventricular and myocardial regions.
  • Assessed feature variability using distribution similarity indices and two classification tasks (center identification and healthy vs. HCM classification).

Main Results:

  • The ComBat harmonization technique effectively removed center-specific information from radiomics features but slightly decreased classification performance.
  • Piecewise linear histogram matching normalization yielded features with superior generalizability for classification, achieving balanced accuracies between 0.78 ± 0.08 and 0.79 ± 0.09.
  • Unnormalized radiomics features resulted in the poorest classification performance, with balanced accuracies ranging from 0.45 ± 0.28 to 0.60 ± 0.22.

Conclusions:

  • Removing center-related information from radiomics features does not inherently guarantee improved classification generalizability.
  • Piecewise linear histogram matching demonstrates potential for enhancing the clinical utility of multi-center radiomics studies.
  • Further research is needed to optimize normalization strategies for robust multi-center radiomics analysis in cardiology.