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...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

You might also read

Related Articles

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

Sort by
Same author

An evaluation of brain volume and cortical thickness measurement at 0.55 T.

Magma (New York, N.Y.)·2026
Same author

Imaging Near Spinal Fixation Hardware at 0.55 T Compared With 3 T.

Journal of magnetic resonance imaging : JMRI·2026
Same author

Microglial clonal dynamics and the impact of clonal hematopoiesis in autologously transplanted rhesus macaques.

Cell reports·2026
Same author

A vendor-neutral functional MRI acquisition protocol for multi-site studies.

Aperture neuro·2026
Same author

Optimization of fetal brain MRI at 0.55 T with slice-to-volume reconstruction.

Magma (New York, N.Y.)·2026
Same author

CROSS-MODAL FINE-TUNING OF 3D CONVOLUTIONAL FOUNDATION MODELS FOR ADHD CLASSIFICATION WITH LOW-RANK ADAPTATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026

Related Experiment Video

Updated: Jul 13, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Accelerating dynamic spiral MRI by algebraic reconstruction from undersampled k--t space.

Taehoon Shin1, Jon-Fredrik Nielsen, Krishna S Nayak

  • 1University of Southern California, Los Angeles, CA 90089, USA. taehoons@usc.edu

IEEE Transactions on Medical Imaging
|July 26, 2007
PubMed
Summary

Dynamic MRI uses interleaved k-space sampling to improve temporal resolution, but causes aliasing. This study presents an algebraic framework and conjugate gradient method to unalias images, reducing motion artifacts and enhancing cardiac structure depiction.

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Pulmonary Structural MRI using Free-Breathing, Self-Gated Ultra-short Echo Time Imaging
05:07

Pulmonary Structural MRI using Free-Breathing, Self-Gated Ultra-short Echo Time Imaging

Published on: September 6, 2024

Related Experiment Videos

Last Updated: Jul 13, 2026

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Pulmonary Structural MRI using Free-Breathing, Self-Gated Ultra-short Echo Time Imaging
05:07

Pulmonary Structural MRI using Free-Breathing, Self-Gated Ultra-short Echo Time Imaging

Published on: September 6, 2024

Area of Science:

  • Medical Imaging
  • Biophysics
  • Signal Processing

Background:

  • Dynamic MRI requires high temporal resolution for imaging moving organs.
  • Undersampled k-space acquisition improves temporal resolution but introduces aliasing artifacts.
  • Spiral imaging is a common technique for dynamic MRI acquisition.

Purpose of the Study:

  • To algebraically and graphically describe the aliasing process in dynamic undersampled spiral MRI.
  • To formulate and solve the unaliasing problem using prior knowledge and efficient numerical methods.
  • To demonstrate the effectiveness of the proposed method in reducing motion artifacts and improving image quality.

Main Methods:

  • Algebraic and graphical description of aliasing in 3-D xyf space.
  • Formulation of unaliasing as independent linear inversions.
  • Application of bounded support regions as prior knowledge.
  • Implementation of a fast conjugate gradient (CG) method for numerical inversion.
  • Validation through numerical simulations and in vivo spiral twofold undersampling experiments.

Main Results:

  • Successful algebraic and graphical characterization of the aliasing process.
  • Demonstrated reduction of motion artifacts and improved depiction of fine cardiac structures in vivo.
  • Achieved motion artifact and blur reduction comparable to simple filtering.
  • Showcased the flexibility of the algebraic framework for incorporating additional acceleration techniques.

Conclusions:

  • The proposed algebraic framework effectively unaliases dynamic undersampled spiral MRI data.
  • The method significantly reduces motion artifacts, enhancing diagnostic capabilities.
  • The conjugate gradient implementation provides a computationally efficient solution for large-scale problems.
  • This approach offers greater flexibility than traditional filtering methods for dynamic MRI acceleration.