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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

769
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...
769

You might also read

Related Articles

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

Sort by
Same author

Boundary-aware and discrepancy-guided dynamic pseudo-labeling with consistency learning for semi-supervised 3D TOF-MRA cerebrovascular segmentation.

Physics in medicine and biology·2026
Same author

A multimodal vision-language model for generalizable annotation-free pathology localization.

Nature biomedical engineering·2026
Same author

Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation.

Research (Washington, D.C.)·2025
Same author

Cross-sequence semi-supervised learning for multi-parametric MRI-based visual pathway delineation.

Physics in medicine and biology·2025
Same author

Mammo-AGE: deep learning estimation of breast age from mammograms.

Nature communications·2025
Same author

Fast and Accurate Abdominal PDFF and R2* Mapping With Model-Fitted Flip Angle Modulation and Simultaneous Multi-Slice (SMS) 2D Imaging.

Magnetic resonance in medicine·2025
Same journal

Multi-Contrast Human Brain CEST MRI at 11.7 T: First In Vivo Demonstration.

Magnetic resonance in medicine·2026
Same journal

Suppression of Oscillation and Ghosting in RF-Spoiled Gradient-Echo-Based Dynamic Imaging.

Magnetic resonance in medicine·2026
Same journal

A Simple, Dynamic Geometric Phantom for MRI and CT Reconstruction Pipelines: Beyond Shepp-Logan.

Magnetic resonance in medicine·2026
Same journal

7T 3D-EPI PCASL With High SNR Efficiency and Robustness to Through-Plane B<sub>0</sub> Field Gradients.

Magnetic resonance in medicine·2026
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
See all related articles

Related Experiment Video

Updated: Feb 17, 2026

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

1.2K

Improved parallel image reconstruction using feature refinement.

Jing Cheng1,2, Sen Jia1,2, Leslie Ying3

  • 1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.

Magnetic Resonance in Medicine
|December 2, 2017
PubMed
Summary
This summary is machine-generated.

A new feature refinement method enhances Magnetic Resonance Imaging (MRI) reconstruction from undersampled data. This technique improves image quality and preserves fine details, outperforming standard methods.

Keywords:
MRIcompressed sensingfeature refinementparallel imaging reconstruction

More Related Videos

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

8.9K
Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction
12:38

Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction

Published on: August 9, 2011

17.9K

Related Experiment Videos

Last Updated: Feb 17, 2026

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

1.2K
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

8.9K
Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction
12:38

Structure of HIV-1 Capsid Assemblies by Cryo-electron Microscopy and Iterative Helical Real-space Reconstruction

Published on: August 9, 2011

17.9K

Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)

Background:

  • Compressed Sensing (CS) and parallel imaging (pMRI) accelerate MRI acquisition.
  • Undersampled data in CS-pMRI often leads to reduced image quality and loss of fine details.

Purpose of the Study:

  • To develop a novel feature refinement method for MR reconstruction.
  • To improve image quality and preserve detailed information from highly undersampled multichannel MRI data.

Main Methods:

  • Applied a feature refinement technique using feature descriptors to capture useful information from residual images.
  • Utilized texture and structure descriptors to recognize different image features.
  • Validated the method on in vivo data using three different multicoil reconstruction techniques.

Main Results:

  • Reconstruction methods incorporating feature refinement demonstrated improved image quality.
  • Enhanced detail restoration was observed compared to original reconstruction methods.
  • Lower root mean square error and high frequency error norm values verified the improvements.

Conclusions:

  • A simple and effective method for preserving detailed information in CS-pMRI was proposed.
  • The feature refinement technique significantly improves reconstruction quality.
  • The method shows superior performance in detail preservation compared to reconstructions without feature refinement.