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

Echo01:06

Echo

980
The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...
980
What is Variation?01:14

What is Variation?

18.6K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.6K
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

28.5K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
28.5K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.9K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.9K
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Variation01:19

Variation

8.0K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.0K

You might also read

Related Articles

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

Sort by
Same author

Photo-induced proton release of spiropyran-derived nanomaterials for mRNA delivery in hard-to-transfect cells.

Smart molecules : open access·2026
Same author

Divergent roles of SPOP and CHD1 in ACSL4 regulation reveal context-dependent vulnerabilities for targeting ferroptosis.

Nature communications·2026
Same author

Short-term outcomes of intracranial angioplasty with the Neuroform EZ stent in patients with hypoperfusion-related symptomatic intracranial atherosclerotic stenosis.

BMC neurology·2026
Same author

Novel nitric oxide dismutase drives effective NO degradation in oxygenic denitrification.

Ecotoxicology and environmental safety·2026
Same author

Chronic Radiation Dermatitis After Breast Cancer Radiotherapy: Mechanistic Insights, Therapeutic Challenges, and Emerging Roles of Piezo1-Mediated Mechanotransduction.

Journal of inflammation research·2026
Same author

Masticatory function, inflammation, and mortality after stroke: A prospective cohort study.

Medicine·2026
Same journal

Erratum for: Prediction of Lobar Emphysema Progression with a CT-Based Foundational Model.

Radiology·2026
Same journal

Erratum for: Associations of MRI-derived Paraspinal IMAT and LMM with Cardiometabolic Risk Factors: Results from a German Cohort.

Radiology·2026
Same journal

Erratum for: Blue Rubber Bleb Nevus Syndrome.

Radiology·2026
Same journal

Redefining the Clinical Role of MRI in Endometrial Cancer Staging.

Radiology·2026
Same journal

To Ablate or Not to Ablate: The Colorectal Liver Metastasis Question.

Radiology·2026
Same journal

The Limits of Radiologic Categorization in Pulmonary Nonsolid Nodules.

Radiology·2026
See all related articles

Related Experiment Video

Updated: Feb 7, 2026

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

763

Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks.

Feiyu Chen1, Valentina Taviani1, Itzik Malkiel1

  • 1From the Departments of Electrical Engineering (F.C., J.M.P.) and Radiology (J.Y.C., J.S., S.T.C., S.S.V.), Stanford University, Stanford, Calif 94305-9510; Global MR Applications and Workflow, GE Healthcare, Menlo Park, Calif (V.T.); GE Global Research Center, Herzliya, Israel (I.M.); Department of Electrical Engineering and Computer Sciences, University of California-Berkeley, Berkeley, Calif (J.I.T.); Department of Radiology, VA Palo Alto Healthcare System, Palo Alto, Calif (S.T.C.); and GE Global Research Center, Niskayuna, NY (C.J.H.).

Radiology
|July 25, 2018
PubMed
Summary
This summary is machine-generated.

A new deep learning method using variational networks (VN) significantly speeds up MRI reconstruction for abdominal imaging. This technique improves image quality, offering higher signal-to-noise ratio and sharpness compared to conventional methods.

More Related Videos

A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis
10:26

A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis

Published on: June 2, 2015

18.0K
Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

2.6K

Related Experiment Videos

Last Updated: Feb 7, 2026

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

763
A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis
10:26

A Multicenter MRI Protocol for the Evaluation and Quantification of Deep Vein Thrombosis

Published on: June 2, 2015

18.0K
Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

2.6K

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accelerated MRI acquisition is crucial for reducing scan times and improving patient comfort.
  • Highly undersampled variable-density single-shot fast spin-echo (ssFSE) sequences offer speed but often suffer from lower image quality.
  • Deep learning approaches show promise for enhancing reconstruction in accelerated imaging.

Purpose of the Study:

  • To develop and evaluate a deep learning-based reconstruction method, variational network (VN), for highly undersampled ssFSE abdominal imaging.
  • To compare the reconstruction speed and image quality of VN against conventional parallel imaging and compressed sensing (PICS).
  • To assess the clinical feasibility of the VN approach for abdominal MRI.

Main Methods:

  • A variational network (VN) was trained using data from 130 patients undergoing abdominal MRI with a 3.0-T imager.
  • Coronal variable-density ssFSE sequences were acquired with 3.25x acceleration.
  • Reconstruction performance was evaluated in 27 patients, comparing VN to PICS, with blinded radiologist assessment of image quality metrics.

Main Results:

  • Variational network (VN) reconstruction was significantly faster (0.19s ± 0.04s) than PICS (5.60s ± 1.30s).
  • VN demonstrated improved perceived signal-to-noise ratio (P = .01) and sharpness (P < .001).
  • Overall image quality was superior with VN compared to PICS (P = .02), with no significant difference in contrast or artifacts.

Conclusions:

  • Variational network (VN) reconstruction accelerates the process for accelerated ssFSE abdominal MRI.
  • The VN approach enhances overall image quality, signal-to-noise ratio, and sharpness.
  • VN represents a feasible and effective deep learning method for improving abdominal MRI reconstruction.