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

Predictive cardiac motion modeling and correction with partial least squares regression.

Nicholas A Ablitt1, Jianxin Gao, Jennifer Keegan

  • 1Royal Society/Wolfson Foundation Medical Image Computing Laboratory, Department of Computing, Imperial College London, London SW7 2BZ, U.K.

IEEE Transactions on Medical Imaging
|October 21, 2004
PubMed
Summary

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

Accelerating Cardiac Diffusion Tensor Imaging With a U-Net Based Model: Toward Single Breath-Hold.

Journal of magnetic resonance imaging : JMRI·2022
Same author

Development of a cardiovascular magnetic resonance-compatible large animal isolated heart model for direct comparison of beating and arrested hearts.

NMR in biomedicine·2022
Same author

Minimisation of slab-selective radiofrequency excitation pulse durations constrained by an acceptable aliasing coefficient.

Magnetic resonance imaging·2021
Same author

Initial investigation of free-breathing 3D whole-heart stress myocardial perfusion MRI.

Global cardiology science & practice·2021
Same author

Diffusion Tensor Cardiovascular Magnetic Resonance in Cardiac Amyloidosis.

Circulation. Cardiovascular imaging·2020
Same author

Automating in vivo cardiac diffusion tensor postprocessing with deep learning-based segmentation.

Magnetic resonance in medicine·2020

This study introduces a novel predictive cardiac motion modeling technique using partial least squares regression. The method accurately predicts respiratory-induced cardiac deformation for improved high-resolution cardiac imaging.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Cardiovascular Research

Background:

  • Respiratory motion significantly degrades cardiac imaging quality.
  • Accurate cardiac motion modeling is crucial for high-resolution cardiovascular imaging.

Purpose of the Study:

  • To develop a predictive model for respiratory-induced cardiac deformation.
  • To enable real-time motion tracking and correction in cardiac imaging.

Main Methods:

  • Utilized partial least squares regression to model relationships between surface intensity traces and cardiac deformation.
  • Extracted latent variables to predict cardiac motion from coupled surface signals.
  • Enabled cross-modality reconstruction of patient-specific motion models.

Related Experiment Videos

Main Results:

  • Demonstrated accurate prediction of cardiac motion despite poor correlation of individual surface traces.
  • Validated the motion and deformation modeling technique using 3-D MRI data.
  • Showcased the potential for real-time prospective motion tracking and correction.

Conclusions:

  • The proposed technique effectively models and corrects respiratory-induced cardiac deformation.
  • This method enhances the quality of high-resolution cardiac imaging.
  • The approach offers a robust solution for real-time cardiac motion management.