Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

Imaging Studies IV: Magnetic Resonance Imaging

Imaging Studies IV: Magnetic Resonance Imaging

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,...

Imaging Studies I: CT and MRI

Imaging Studies I: CT and MRI

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...

You might also read

Related Articles

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

Sort by

Same author

Multi-View Stenosis Classification Leveraging Transformer-Based Multiple-Instance Learning Using Real-World Clinical Data.

IEEE transactions on medical imaging·2026

Same author

A Trojan horse inside the gates: Alien-native mating interactions in mixed populations of Solidago taxa.

Plant biology (Stuttgart, Germany)·2025

Same author

Synergistic feature selection and distributed classification framework for high-dimensional medical data analysis.

MethodsX·2025

Same author

Environmental variables influence the relationship between maternal depressive symptoms and toddlers' neurocognitive and affective outcomes.

Journal of affective disorders·2024

Same author

Molecular signatures of cortical expansion in the human foetal brain.

Nature communications·2024

Same author

UNITY: A low-field magnetic resonance neuroimaging initiative to characterize neurodevelopment in low and middle-income settings.

Developmental cognitive neuroscience·2024

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Published on: May 19, 2023

Combining morphological information in a manifold learning framework: application to neonatal MRI.

P Aljabar¹, R Wolz, L Srinivasan

¹Department of Computing, Imperial College London, UK. Paul.Aljabar@Imperial.ac.uk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|October 1, 2010

Summary

This study introduces a new framework using multiple MR image measures for a comprehensive neonatal brain morphology representation. Combining shape and appearance data improves characterization of brain development trajectories.

More Related Videos

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Related Experiment Videos

Last Updated: Jun 8, 2026

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Micro-CT Imaging and Morphometric Analysis of Mouse Neonatal Brains

Published on: May 19, 2023

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Area of Science:

Neuroimaging
Medical Image Analysis
Developmental Neuroscience

Background:

Single MR image measures are insufficient for comprehensive morphological characterization.
Neonatal brain development involves rapid changes in shape and image appearance.

Purpose of the Study:

To develop a framework combining multiple MR image measures for improved population representation.
To characterize neonatal brain development trajectories using a unified morphological representation.

Main Methods:

Utilizing manifold learning to generate coordinate embeddings from multiple MR image measures.
Combining shape (deformation metric) and appearance (image similarity) measures.
Applying the framework to neonatal brain MRI data.

Main Results:

The combined embeddings provide biologically plausible and consistent representations of neonatal brain morphology.
Orthogonal correlations suggest independent morphological feature representation.
Improved correlations with clinical data demonstrate the benefit of combining embeddings.

Conclusions:

The proposed framework offers a superior method for characterizing neonatal brain development.
Integrating diverse MR image measures enhances the understanding of complex morphological changes.
This approach has potential for improved clinical data correlation and developmental analysis.