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

10.3K
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...
10.3K
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

1.2K
When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
1.2K
Upsampling01:22

Upsampling

696
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...
696

You might also read

Related Articles

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

Sort by
Same author

Radiation-free assessment of the 3D morphology of the adolescent scoliotic spine: a feasibility study using MRI derived quantitative synthetic CT.

Spine deformity·2026
Same author

Confounding factors in the generalisation of synthetic CT to diagnostic spine MRI.

Physics in medicine and biology·2026
Same author

Spatiotemporal Encoding With Nonlinear Gradient Hardware Using Pulseq: From Principles to Practical Demonstration.

Magnetic resonance in medicine·2026
Same author

Safety and accuracy of cervical pedicle screw navigation using artificial intelligence-generated, MRI-based synthetic CT versus conventional CT.

Journal of neurosurgery. Spine·2025
Same author

Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial.

European radiology·2025
Same author

The safety and accuracy of radiation-free spinal navigation using a short, scoliosis-specific BoneMRI-protocol, compared to CT.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2025

Related Experiment Video

Updated: Mar 28, 2026

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

2.0K

Evaluation of Variable Density and Data-Driven K-Space Undersampling for Compressed Sensing Magnetic Resonance

Frank Zijlstra1, Max A Viergever, Peter R Seevinck

  • 1From the Image Sciences Institute, UMC Utrecht, Utrecht, the Netherlands.

Investigative Radiology
|December 18, 2015
PubMed
Summary

Data-driven undersampling patterns improve compressed sensing (CS) magnetic resonance imaging reconstruction quality. An iterative design method, trained on fully sampled scans, yielded the highest quality, avoiding suboptimal results.

More Related Videos

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

20.2K
Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement
09:43

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Published on: November 7, 2017

10.0K

Related Experiment Videos

Last Updated: Mar 28, 2026

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

2.0K
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

20.2K
Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement
09:43

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

Published on: November 7, 2017

10.0K

Area of Science:

  • Medical Imaging
  • Biophysics
  • Signal Processing

Background:

  • Compressed sensing (CS) magnetic resonance imaging (MRI) allows for faster scans by undersampling k-space data.
  • Suboptimal undersampling patterns can lead to decreased reconstruction quality.
  • Developing optimal undersampling strategies is crucial for efficient and accurate CS-MRI.

Purpose of the Study:

  • To investigate the impact of variable density and data-driven k-space undersampling patterns on CS-MRI reconstruction quality.
  • To provide recommendations for avoiding suboptimal CS reconstructions.
  • To evaluate the effectiveness of data-driven methods for improving reconstruction quality.

Main Methods:

  • Compared random variable density and Poisson disk undersampling patterns with fully sampled data.
  • Evaluated sampling coherence in relation to reconstruction error.
  • Implemented and compared three data-driven undersampling methods: Monte Carlo optimization, power spectrum-based probability calculation, and iterative design.
  • Assessed reconstruction quality using normalized root-mean-square error and mean structural similarity index measure.

Main Results:

  • Optimal sampling density is data-dependent and choosing a non-optimal density decreases reconstruction quality.
  • No significant correlation was found between sampling coherence and reconstruction error.
  • Data-driven methods, particularly the iterative design method, significantly improved reconstruction quality.
  • The size of the training set had a minimal impact on reconstruction quality.

Conclusions:

  • Data-driven undersampling methods can prevent suboptimal CS-MRI reconstructions when trained with fully sampled data.
  • The iterative design method demonstrated the highest reconstruction quality among the evaluated data-driven approaches.