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

8.8K
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...
8.8K

You might also read

Related Articles

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

Sort by
Same author

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017
Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017
Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017
Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017
Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017
Same author

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.

IEEE transactions on medical imaging·2017

Related Experiment Video

Updated: Dec 26, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.8K

Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data.

Yoonmi Hong1, Geng Chen1, Pew-Thian Yap1

  • 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Information Processing in Medical Imaging : Proceedings of the ... Conference
|March 13, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for faster diffusion MRI scans by reconstructing full images from undersampled slices. Graph convolutional neural networks enable high acceleration factors with minimal data loss.

Keywords:
Accelerated acquisitionAdversarial learningDiffusion MRIGraph CNNSuper resolution

More Related Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.0K
Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

12.1K

Related Experiment Videos

Last Updated: Dec 26, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.8K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.0K
Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

12.1K

Area of Science:

  • Medical Imaging
  • Neuroscience
  • Biophysics

Background:

  • Diffusion MRI (dMRI) is crucial for analyzing tissue microstructure.
  • However, dMRI's long acquisition times limit its clinical applicability.
  • Current methods often require lengthy scanning protocols.

Purpose of the Study:

  • To develop an effective method for reconstructing diffusion-weighted (DW) MRI volumes from slice-undersampled data.
  • To enable significant acceleration of dMRI acquisition without compromising image quality.
  • To leverage complementary information across diffusion wavevectors for improved reconstruction.

Main Methods:

  • Acquisition of only a subsample of equally-spaced slices instead of full DW image volumes.
  • Utilizing graph convolutional neural networks (GCNNs) to process undersampled data.
  • Harnessing complementary information from DW volumes with different diffusion wavevectors.

Main Results:

  • Successful reconstruction of full DW MRI volumes from slice-undersampled data.
  • Demonstration of a high acceleration factor, up to 5x, achievable with the proposed method.
  • Minimal information loss observed during the reconstruction process, preserving key microstructural details.

Conclusions:

  • The presented method offers a viable solution for accelerating dMRI acquisition.
  • GCNNs effectively reconstruct dMRI data from undersampled slices by utilizing multi-vector diffusion information.
  • This approach has the potential to significantly reduce scan times in clinical settings.