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

Magnetic Resonance Imaging01:24

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...

You might also read

Related Articles

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

Sort by
Same author

Machine learning driven forward-reverse design of Ag-ZnO-PEEK nanocomposites for sustainable biomass and lipid enhancement in Chlorella vulgaris AK_123 with integrated anti-bacterial activity.

Scientific reports·2026
Same author

Microelectrode arrays enable directional stereo-EEG during kainate-mediated seizures.

bioRxiv : the preprint server for biology·2026
Same author

Deep learning-assisted ultrathin broadband vanadium dioxide-based polarization-insensitive terahertz metamaterial absorber.

Scientific reports·2026
Same author

Advancing machine learning tools for early prediction and clinical diagnosis of pre-eclampsia.

Pregnancy hypertension·2025
Same author

TPMT Genotyping in 1000 Indian Patients: 14-Year Experience from a Tertiary-Care Hospital.

EJIFCC·2025
Same author

Bi-functional carbonaceous hybrid nanocomposites with anticancer and antibacterial potential: synthesis, characterization, and cytotoxicity assessment.

Naunyn-Schmiedeberg's archives of pharmacology·2025
Same journal

Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes.

Information processing in medical imaging : proceedings of the ... conference·2026
Same journal

Cycle-Consistent Zero-Shot Through-Plane Super-Resolution for Anisotropic Head MRI.

Information processing in medical imaging : proceedings of the ... conference·2026
Same journal

Brightness-Invariant Tracking Estimation in Tagged MRI.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

Using Multiple Instance Learning to Build Multimodal Representations.

Information processing in medical imaging : proceedings of the ... conference·2025
Same journal

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds.

Information processing in medical imaging : proceedings of the ... conference·2024
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

A framework for brain registration via simultaneous surface and volume flow.

Anand Joshi1, Richard Leahy, Arthur W Toga

  • 1Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA 90095, USA. anand.joshi@loni.ucla.edu

Information Processing in Medical Imaging : Proceedings of the ... Conference
|August 22, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel brain MRI registration method using a 3D harmonic map to normalize shape differences. The approach accurately aligns complex sulcal patterns and subcortical structures for improved volumetric registration.

More Related Videos

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Related Experiment Videos

Last Updated: Jun 20, 2026

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging
11:50

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Published on: February 4, 2022

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Anatomy

Background:

  • Volumetric registration of brain MR images is complex due to variations in sulcal patterns.
  • Accurate registration is crucial for comparing brain structures and detecting changes.

Purpose of the Study:

  • To develop a novel volumetric registration method for brain MR images.
  • To normalize shape differences using an intermediate parameter space.

Main Methods:

  • Generated 3D harmonic maps of brain volumes to a unit ball as an intermediate space.
  • Employed a finite element method (FEM) with tetrahedral volumetric and surface meshes.
  • Achieved simultaneous surface and volume registration using cortical features and intensity.

Main Results:

  • The method effectively aligns convoluted sulcal folding patterns.
  • Subcortical structures were accurately registered.
  • Evaluation demonstrated good overlap between segmented structures in coregistered brains.

Conclusions:

  • The proposed method offers a robust solution for volumetric brain MRI registration.
  • Normalization via harmonic maps improves alignment of complex anatomical variations.
  • FEM implementation enables simultaneous registration of surface and volumetric data.