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

7.5K
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...
7.5K
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

417
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
417
Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.0K
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

529
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
529
Stratified Sampling Method01:16

Stratified Sampling Method

11.7K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.7K
Computed Tomography01:10

Computed Tomography

7.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.6K

You might also read

Related Articles

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

Sort by
Same author

Systematic review on machine learning applications in Paralympic sports: current practice and future research.

Disability and rehabilitation. Assistive technology·2026
Same author

In vivo metabolic tagging and targeting of circulating red blood cells.

Nature communications·2026
Same author

Multimodal mass spectrometry imaging for plaque- and region-specific neurolipidomics in Alzheimer's disease mouse models.

Nature communications·2025
Same author

<i>In Vivo</i> Metabolic Tagging and Targeting of Circulating Red Blood Cells.

bioRxiv : the preprint server for biology·2025
Same author

Integrating Model-Based Reconstruction and Deep Learning for Accelerating Mass Spectrometry Imaging.

Analytical chemistry·2025
Same author

High-Throughput Fluorescence-Guided Sequential Single-Cell MALDI-ICC Mass Spectrometry.

Analytical chemistry·2025
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
Same journal

A Low-Cost Wearable TI-TACS Stimulator With Bipolar Quadratic-Boost Converter for Current Stimulation Validation in the Rat Brain.

IEEE transactions on bio-medical engineering·2026
Same journal

EMG-Based Gait Estimation Using Koopman-Inspired Method.

IEEE transactions on bio-medical engineering·2026
Same journal

Soft Everting Robots for Medical Applications: A Review.

IEEE transactions on bio-medical engineering·2026
Same journal

Arterial spin labeling cerebral blood flow quantification from quantitative transport mapping based on multiscale fluid mechanics simulation and deep learning.

IEEE transactions on bio-medical engineering·2026
See all related articles

Related Experiment Video

Updated: Apr 25, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K

INTERVENTIONAL MRI WITH SPARSE SAMPLING USING UNION-OF-SUBSPACES.

S Derin Babacan1, Fan Lam1, Xi Peng1

  • 1Beckman Institute for Advanced Science and Technology, and, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign.

IEEE Transactions on Bio-Medical Engineering
|August 26, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel union-of-subspaces signal model to accelerate interventional magnetic resonance imaging (MRI). The method enhances imaging speed and quality by leveraging sparse sampling and prior information, applicable to various imaging scenarios.

Keywords:
Interventional MRIfast imagingsparse samplingunion of subspaces

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K

Related Experiment Videos

Last Updated: Apr 25, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Signal Processing

Background:

  • Interventional magnetic resonance imaging (MRI) faces limitations in imaging speed, hindering real-time applications.
  • Current MRI techniques struggle to balance speed, resolution, and image quality.

Purpose of the Study:

  • To address the limited imaging speed in interventional MRI.
  • To introduce and validate a new signal model for accelerated MRI acquisition.

Main Methods:

  • Developed a novel signal model termed "union-of-subspaces".
  • Utilized sparse sampling strategies combined with prior information within the new model.
  • Validated the approach using simulations on real interventional MRI data.

Main Results:

  • Achieved significantly improved imaging speed in interventional MRI.
  • Demonstrated high-quality imaging results comparable to conventional methods.
  • The union-of-subspaces model proved effective in accelerating MRI acquisition.

Conclusions:

  • The union-of-subspaces model offers a flexible and effective solution for accelerating interventional MRI.
  • This approach holds potential for enhancing various other medical imaging applications.
  • The method successfully improves imaging speed without compromising image quality.