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

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

Scalable hypothalamic neuron differentiation from human pluripotent stem cells suitable for modeling metabolic disorders.

Stem cell reports·2026
Same author

Genetics of growth rate in induced pluripotent stem cells.

Stem cell reports·2026
Same author

Multiscale connectivity framework for working memory network in paediatric acute lymphoblastic leukaemia survivors.

Brain communications·2026
Same author

Non-linear dynamics in ECG: a novel approach for robust classification of cardiovascular disorders.

NPJ cardiovascular health·2026
Same author

Comparing orientation-dependent transverse relaxation at 3 T and 7 T: Deciphering anisotropic relaxation mechanisms in white matter.

Magnetic resonance imaging·2026
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Related Experiment Video

Updated: Apr 29, 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

946

Adaptive k-space sampling design for edge-enhanced DCE-MRI using compressed sensing.

Rajikha Raja1, Neelam Sinha1

  • 1International Institute of Information Technology, Bangalore, India.

Magnetic Resonance Imaging
|May 23, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive k-space sampling and edge-enhanced reconstruction method for dynamic contrast-enhanced MRI (DCE-MRI). The technique significantly improves image quality from undersampled data, crucial for faster scans.

Keywords:
Adaptive k-space samplingCS-based image reconstructionDCE-MRIGradient priors

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

18.6K
Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
17:16

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

12.3K

Related Experiment Videos

Last Updated: Apr 29, 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

946
Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

18.6K
Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
17:16

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

12.3K

Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging
  • Image Reconstruction

Background:

  • Dynamic contrast-enhanced MRI (DCE-MRI) faces a critical trade-off between spatial and temporal resolution due to acquisition time limitations.
  • Undersampling k-space data is necessary, requiring advanced reconstruction techniques to maintain image quality.

Purpose of the Study:

  • To develop a novel reconstruction method for high-quality DCE-MRI from undersampled k-space data.
  • To improve the spatial and temporal resolution balance in DCE-MRI acquisition.

Main Methods:

  • An adaptive k-space sampling lattice was designed based on static high-resolution data energy distribution.
  • An edge-enhanced reconstruction technique incorporated gradient information from static data into a compressed sensing-based total variation minimization scheme.
  • The method was tested on 7 real dynamic time series (2 breast, 5 abdomen datasets).

Main Results:

  • The proposed method achieved significant performance improvements across various quality metrics with only 10% data availability.
  • Average improvements in Universal Image Quality Index (UQI) and Structural Similarity Index Metric (SSIM) were up to 28% and 24% for breast data.
  • Average UQI and SSIM improvements were approximately 17% and 9% for abdomen data compared to baseline reconstruction.

Conclusions:

  • The developed adaptive k-space sampling and edge-enhanced reconstruction method effectively enhances DCE-MRI quality from undersampled data.
  • This approach offers a viable solution to the spatial-temporal resolution trade-off in DCE-MRI.
  • The method demonstrates substantial improvements in image quality metrics for both breast and abdomen imaging.