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 Videos

Motion estimation from tagged MR image sequences.

J L Prince1, E R McVeigh

  • 1Johns Hopkins Univ., Baltimore, MD.

IEEE Transactions on Medical Imaging
|January 1, 1992
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

Coronary artery calcium and cardiovascular disease prediction by scanner type: the multi-ethnic study of atherosclerosis.

Clinical radiology·2022
Same author

Deformable Known Component Model-Based Reconstruction for Coronary CT Angiography.

Proceedings of SPIE--the International Society for Optical Engineering·2021
Same author

MTT and Blood-Brain Barrier Disruption within Asymptomatic Vascular WM Lesions.

AJNR. American journal of neuroradiology·2021
Same author

Inter-scanner Variation Independent Descriptors for Constrained Diffeomorphic Demons Registration of Retina OCT.

Proceedings of SPIE--the International Society for Optical Engineering·2019
Same author

A 3D Computational Head Model Under Dynamic Head Rotation and Head Extension Validated Using Live Human Brain Data, Including the Falx and the Tentorium.

Annals of biomedical engineering·2019
Same author

Performance evaluation of MIND demons deformable registration of MR and CT images in spinal interventions.

Physics in medicine and biology·2016
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
See all related articles

This study presents a new method using magnetic resonance (MR) tagging and optical flow to accurately reconstruct motion from MR images. The technique enhances motion tracking for individual particles and segmented objects, overcoming challenges in image analysis.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biophysics

Background:

  • Magnetic Resonance (MR) tagging creates visible patterns in images to track tissue motion.
  • Reconstructing motion from image sequences is challenging due to the aperture problem and signal decay.
  • Existing methods struggle with accurate motion tracking over time.

Purpose of the Study:

  • To develop a novel method for motion reconstruction from tagged MR image sequences.
  • To address the challenge of tag pattern decay in MR imaging.
  • To improve the accuracy of tracking both individual particles and segmented objects.

Main Methods:

  • Utilized MR tagging to generate textured patterns for enhanced motion visualization.
  • Developed and implemented a new optical flow algorithm to compensate for tag pattern decay.

Related Experiment Videos

  • Employed a recursively updated motion reference map to track particle movement over time.

    • Main Results:

    • Successfully reconstructed motion from simulated and actual MR phantom data.
    • The developed optical flow algorithm effectively compensated for tag pattern decay.
    • Demonstrated accurate tracking of individual particles and segmented objects.

    Conclusions:

    • The proposed method enables robust motion reconstruction from tagged MR images.
    • This technique enhances the capabilities of MR imaging for dynamic studies.
    • Offers improved motion analysis in biological and physical systems.