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

miniMORPH: A Morphometry Pipeline for Low-Field MRI in Infants.

Human brain mapping·2026
Same author

Assessing cognition in autistic youth with and without attention-deficit/hyperactivity disorder using the NIH Toolbox Cognition Battery: An Environmental influences on Child Health Outcomes-Wide Cohort Study.

JCPP advances·2026
Same author

Correction to: Ultra-low-field brain MRI morphometry: Test-retest reliability and correspondence to high-field MRI.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Associations of Maternal Pre-Pregnancy BMI With Frontostriatal Connectivity in Young Children.

Pediatric obesity·2026
Same author

Brain volume trajectories in young children are associated with polygenic scores for late-onset Alzheimer's disease risk.

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

The Peripheral Epigenome Predicts White Matter Volume Contingent on Developmental Stage: An ECHO Study.

Molecular neurobiology·2025
Same journal

Automated Diagnosis of Breast Cancer Using Deep Learning Techniques Applied to Digital Mammography and Magnetic Resonance Images.

Topics in magnetic resonance imaging : TMRI·2026
Same journal

Assessment of Brain Tumor Response to Radiotherapy Using Noninvasive Spectroscopic Magnetic Resonance Imaging Techniques.

Topics in magnetic resonance imaging : TMRI·2026
Same journal

Machine Learning-Based Detection of EGFR Mutation and HER2 Overexpression in Metastatic Brain Adenocarcinoma: Systematic Review and Meta-Analysis.

Topics in magnetic resonance imaging : TMRI·2025
Same journal

Oxygen Saturation, Heart Rate, and Anxiety Levels Among Claustrophobic and Non-Claustrophobic Patients Undergoing Closed and Open MRI: A Comparative Study.

Topics in magnetic resonance imaging : TMRI·2025
Same journal

Preclinical Investigations Toward Gd-free Molecularly Targeted Dual-Modal, MRI Dynamic (DCE-MRI)/Optical Imaging Contrast Agent for Cardiac Angiosarcoma.

Topics in magnetic resonance imaging : TMRI·2025
Same journal

Understanding the Independent Risk Factors of Anterior Shoulder Dislocation Using MRI.

Topics in magnetic resonance imaging : TMRI·2025
See all related articles

Related Experiment Video

Updated: Jun 1, 2026

Quantitative Autoradiographic Method for Determination of Regional Rates of Cerebral Protein Synthesis In Vivo
11:01

Quantitative Autoradiographic Method for Determination of Regional Rates of Cerebral Protein Synthesis In Vivo

Published on: June 28, 2019

Quantitative relaxometry of the brain.

Sean C L Deoni1

  • 1Centre for Neuroimaging Research, King's College London, Institute of Psychiatry, London, United Kingdom. sdeoni@brown.edu

Topics in Magnetic Resonance Imaging : TMRI
|May 27, 2011
PubMed
Summary
This summary is machine-generated.

Quantitative magnetic resonance imaging (MRI) relaxometry measures T1 and T2 relaxation times to reveal tissue changes. This review details in vivo measurement techniques for neuroimaging and other applications.

More Related Videos

Real-time Iontophoresis with Tetramethylammonium to Quantify Volume Fraction and Tortuosity of Brain Extracellular Space
10:45

Real-time Iontophoresis with Tetramethylammonium to Quantify Volume Fraction and Tortuosity of Brain Extracellular Space

Published on: July 24, 2017

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts
09:01

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

Published on: September 21, 2014

Related Experiment Videos

Last Updated: Jun 1, 2026

Quantitative Autoradiographic Method for Determination of Regional Rates of Cerebral Protein Synthesis In Vivo
11:01

Quantitative Autoradiographic Method for Determination of Regional Rates of Cerebral Protein Synthesis In Vivo

Published on: June 28, 2019

Real-time Iontophoresis with Tetramethylammonium to Quantify Volume Fraction and Tortuosity of Brain Extracellular Space
10:45

Real-time Iontophoresis with Tetramethylammonium to Quantify Volume Fraction and Tortuosity of Brain Extracellular Space

Published on: July 24, 2017

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts
09:01

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

Published on: September 21, 2014

Area of Science:

  • Biophysics
  • Medical Imaging
  • Neuroscience

Background:

  • Magnetic resonance imaging (MRI) contrast relies on T1 and T2 relaxation times.
  • Quantitative relaxometry offers detailed tissue characterization beyond conventional MRI.
  • Understanding relaxation times links tissue microstructure to biological processes.

Purpose of the Study:

  • Review the biophysical basis of T1, T2, and T2* relaxation.
  • Detail in vivo measurement techniques for quantitative relaxometry.
  • Discuss applications in neuroimaging and other medical fields.

Main Methods:

  • Focus on clinically feasible in vivo measurement techniques.
  • Address potential sources of error and correction methods.
  • Explore combining relaxation data with diffusion tensor imaging.

Main Results:

  • Quantitative relaxometry provides insights into disease, development, and biological processes.
  • In vivo measurement techniques are applicable across various imaging modalities.
  • Integration with other quantitative data enhances tissue characterization.

Conclusions:

  • Quantitative relaxometry is a powerful tool for understanding tissue properties.
  • Standardized measurement techniques are crucial for clinical feasibility.
  • Combining relaxometry with diffusion tensor imaging offers comprehensive brain tissue analysis.