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 Experiment Video

Updated: May 27, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
19:15

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

Advanced neonatal NeuroMRI.

Kenichi Oishi1, Andreia V Faria, Susumu Mori

  • 1The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. koishi@mri.jhu.edu

Magnetic Resonance Imaging Clinics of North America
|November 29, 2011
PubMed
Summary
This summary is machine-generated.

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

Associations of cerebrospinal fluid measures of synaptic function with white matter microstructure and cognition in older adults.

Frontiers in aging neuroscience·2026
Same author

An olfactory-prefrontal cortical circuit supports social recognition.

Research square·2026
Same author

Five-year change in brain metabolism across the spectrum of cognitive impairment in older adults: a quantitative MRI study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Acute-Phase Machine Learning Prediction of 12-Month Aphasia and Discourse Recovery.

medRxiv : the preprint server for health sciences·2026
Same author

Hippocampal subregional texture features associated with Alzheimer's disease severity and cognition.

Brain communications·2026
Same author

Correlation between changes in substructure diffusion tensor imaging and neurocognitive outcomes for pediatric brain tumor survivors.

Neuro-oncology advances·2026
Same journal

Cardiovascular Magnetic Resonance: Innovation, Integration, and Clinical Impact.

Magnetic resonance imaging clinics of North America·2026
Same journal

Advances and Innovations in Cardiovascular Magnetic Resonance.

Magnetic resonance imaging clinics of North America·2026
Same journal

The Future of Cardiac Magnetic Resonance: Navigating Ultra-High and Low-Field Imaging (Part 2).

Magnetic resonance imaging clinics of North America·2026
Same journal

Artificial Intelligence Applications in Cardiac MR Imaging.

Magnetic resonance imaging clinics of North America·2026
Same journal

Climate Change and Globally Sustainable Cardiovascular Magnetic Resonance.

Magnetic resonance imaging clinics of North America·2026
Same journal

Strain Imaging in Heart Failure.

Magnetic resonance imaging clinics of North America·2026
See all related articles

This study explores quantitative analysis of neonatal brain images using structural magnetic resonance (MR) imaging and diffusion tensor imaging. Combining these techniques and creating a neonate brain atlas enables automated segmentation for clinical evaluation.

Area of Science:

  • Neuroimaging
  • Medical Imaging Analysis
  • Developmental Neuroscience

Background:

  • Quantitative analysis of neonatal brain images is crucial for understanding brain development and detecting abnormalities.
  • Structural magnetic resonance (MR) imaging and diffusion tensor imaging (DTI) offer valuable insights into neonatal brain structure and white matter tracts.

Purpose of the Study:

  • To describe the potential and challenges of quantitative analysis of neonatal brain images.
  • To introduce an automated segmentation approach for neonatal brain MR images using a created neonate brain atlas.
  • To discuss the accuracy, benefits, and limitations of atlas-based segmentation.

Main Methods:

  • Utilized multicontrast data from structural MR imaging and DTI.
  • Developed a neonate brain atlas from the combined imaging data.

More Related Videos

A Battery of Motor Tests in a Neonatal Mouse Model of Cerebral Palsy
10:02

A Battery of Motor Tests in a Neonatal Mouse Model of Cerebral Palsy

Published on: November 3, 2016

Intracerebroventricular Viral Injection of the Neonatal Mouse Brain for Persistent and Widespread Neuronal Transduction
10:15

Intracerebroventricular Viral Injection of the Neonatal Mouse Brain for Persistent and Widespread Neuronal Transduction

Published on: September 15, 2014

Related Experiment Videos

Last Updated: May 27, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
19:15

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

Published on: August 25, 2014

A Battery of Motor Tests in a Neonatal Mouse Model of Cerebral Palsy
10:02

A Battery of Motor Tests in a Neonatal Mouse Model of Cerebral Palsy

Published on: November 3, 2016

Intracerebroventricular Viral Injection of the Neonatal Mouse Brain for Persistent and Widespread Neuronal Transduction
10:15

Intracerebroventricular Viral Injection of the Neonatal Mouse Brain for Persistent and Widespread Neuronal Transduction

Published on: September 15, 2014

  • Applied atlas-based segmentation for automated analysis of neonatal brain MR images.

    • Main Results:

    • Combination of structural MR imaging and DTI enhances the potential of neonatal brain studies.
    • A neonate brain atlas facilitates automated segmentation of brain MR images.
    • Atlas-based segmentation offers accurate and reproducible MR imaging quantification.

    Conclusions:

    • Accurate and reproducible MR imaging quantification is achievable through atlas-based segmentation.
    • This approach represents a significant step toward clinical evaluation of the neonatal brain.
    • Further research can build upon this methodology for improved neonatal neuroimaging analysis.