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

Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

You might also read

Related Articles

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

Sort by
Same author

An evolutionary neural architecture search for magnetic resonance image reconstructions.

International journal of computer assisted radiology and surgery·2026
Same author

A voxel-wise uncertainty-guided framework for glioma segmentation using spherical projection-based U-Net and localized refinement.

Medical physics·2026
Same author

PhysMorph: A biomechanical and image-guided deep learning framework for real-time multi-modal liver image registration.

Physics and imaging in radiation oncology·2026
Same author

An exploratory study on integrating radiomics with vision transformers for enhancing medical imaging classification accuracy.

Medical physics·2026
Same author

An Implicit Registration Framework Integrating Kolmogorov-Arnold Networks with Velocity Regularization for Image-Guided Radiation Therapy.

Bioengineering (Basel, Switzerland)·2025
Same author

Finite Element Method-Based Hybrid MRI/CBCT Generation to Improve Liver Stereotactic Body Radiation Therapy Targets Localization Accuracy.

IEEE transactions on radiation and plasma medical sciences·2025
Same journal

A Comparison of Tissue Property Values Estimated Using Conventional Cardiac MRF and MT-Cardiac MRF.

Magnetic resonance in medicine·2026
Same journal

Dependence of the Extra-Cellular Diffusion Coefficient on the Fractions of Neurites and Cell Bodies in Gray Matter.

Magnetic resonance in medicine·2026
Same journal

Triple-Pulse <sup>23</sup>Na MRI Sequence (TriNa) for Simultaneous Acquisition of Spin-Density-Weighted and Fluid-Attenuated Images.

Magnetic resonance in medicine·2026
Same journal

Evaluation of Phantom Doping Materials in Quantitative Susceptibility Mapping.

Magnetic resonance in medicine·2026
Same journal

Design of an 8-Channel Transmit 32-Channel Receive 11.7T Head Coil and Evaluation of SNR Gains.

Magnetic resonance in medicine·2026
Same journal

The Potential for Absolute Temperature Imaging Based on Brain Metabolites Using an FID-Shifting Approach in Gradient Echo Planar Spectroscopic Imaging (GREPSI).

Magnetic resonance in medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 27, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Efficient multiple acquisitions by Skipped Phase Encoding and Edge Deghosting (SPEED) using shared spatial

Zheng Chang1, Qing-San Xiang, Jim Ji

  • 1Department of Radiation Oncology, Duke University, Durham, North Carolina 27710, USA. zheng.chang@duke.edu

Magnetic Resonance in Medicine
|December 20, 2008
PubMed
Summary
This summary is machine-generated.

The advanced Skipped Phase Encoding and Edge Deghosting (SPEED) MRI technique accelerates multiple scans by sharing ghost information between acquisitions. This method improves efficiency and image quality in rapid MRI scans.

More Related Videos

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Related Experiment Videos

Last Updated: Jun 27, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)

Background:

  • MRI scans can suffer from ghosting artifacts.
  • Existing methods like SPEED address ghosting in single acquisitions.

Purpose of the Study:

  • To enhance the SPEED MRI method for accelerating multiple acquisitions.
  • To improve the efficiency of ghost artifact removal in sequential MRI scans.

Main Methods:

  • Developed a SPEED MRI technique acquiring three sparse ghosted edge maps with N/3 undersampling.
  • Modeled ghosting with a double-layer structure and solved equations to determine ghost order indexes.
  • Enabled sharing of ghost order indexes across multiple acquisitions for improved efficiency.

Main Results:

  • Successfully resolved two ghost artifacts per acquisition, producing deghosted images.
  • Demonstrated potential for greater acceleration factors by leveraging similarities in multiple acquisitions.
  • Achieved enhanced efficiency compared to single-acquisition SPEED.

Conclusions:

  • The enhanced SPEED method effectively removes ghosting artifacts in MRI.
  • Sharing ghost information across acquisitions significantly accelerates the imaging process.
  • This development offers a pathway to faster and more efficient multi-acquisition MRI studies.