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

8.1K
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...
8.1K
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

88
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,...
88

You might also read

Related Articles

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

Sort by
Same author

Flexible encoding of multiple task dimensions in human cerebral cortex.

Frontiers in cognition·2026
Same author

The dynamic functional connectivity peak index: Detection of interictal epileptic activity with fMRI.

Epilepsia·2026
Same author

DeepFixel: Crossing white matter fiber identification through spherical convolutional neural networks.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

MEASURING IMPACT OF SUPER-RESOLUTION ON SPINAL CORD MRI SCANS: LESION DETECTION SENSITIVITY, VARIABILITY, AND CLINICAL IMPACT.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same author

Disrupted Coupling of Heart Rate-Dependent Brain Network Switching and Attentional Task Performance in Schizophrenia Spectrum Disorders.

medRxiv : the preprint server for health sciences·2026
Same author

A Novel Therapeutic Mechanism for Nicotine Craving in Schizophrenia.

medRxiv : the preprint server for health sciences·2026
Same journal

Diffusion phantom study of fiber crossings at varied angles reconstructed with ODF-Fingerprinting.

Computational diffusion MRI : MICCAI Workshop·2025
Same journal

Self Supervised Denoising Diffusion Probabilistic Models for Abdominal DW-MRI.

Computational diffusion MRI : MICCAI Workshop·2024
Same journal

FASSt : Filtering via Symmetric Autoencoder for Spherical Superficial White Matter Tractography.

Computational diffusion MRI : MICCAI Workshop·2024
Same journal

Automated Mapping of Residual Distortion Severity in Diffusion MRI.

Computational diffusion MRI : MICCAI Workshop·2024
Same journal

Lesion Normalization and Supervised Learning in Post-traumatic Seizure Classification with Diffusion MRI.

Computational diffusion MRI : MICCAI Workshop·2023
Same journal

Stepwise Stochastic Dictionary Adaptation Improves Microstructure Reconstruction with Orientation Distribution Function Fingerprinting.

Computational diffusion MRI : MICCAI Workshop·2023
See all related articles

Related Experiment Video

Updated: Oct 22, 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

26.5K

Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning.

Vishwesh Nath1, Prasanna Parvathaneni1, Colin B Hansen1

  • 1EECS, Vanderbilt University, Nashville TN 37203, USA.

Computational Diffusion MRI : MICCAI Workshop
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

A new deep learning method, the null space deep network (NSDN), significantly improves the accuracy and reproducibility of brain fiber imaging using diffusion-weighted MRI (DW-MRI). This data-driven approach enhances local fiber reconstruction across different scanners.

Keywords:
CSDDW-MRIDeep LearningDiffusionHARDIHarmonizationInter-ScannerNull Space

More Related Videos

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

10.5K
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

22.7K

Related Experiment Videos

Last Updated: Oct 22, 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

26.5K
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

10.5K
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

22.7K

Area of Science:

  • Neuroimaging
  • Biophysics
  • Machine Learning

Background:

  • Diffusion-weighted magnetic resonance imaging (DW-MRI) enables non-invasive visualization of brain's microstructural architecture.
  • Current methods for reconstructing local fiber orientations from DW-MRI data, such as constrained spherical deconvolution (CSD), often lack reproducibility across different MRI scanners.
  • There is a need for more robust and reproducible techniques for accurate brain fiber analysis.

Purpose of the Study:

  • To introduce a novel data-driven technique, the null space deep network (NSDN), for improved local fiber reconstruction from DW-MRI data.
  • To enhance the accuracy, reproducibility, and generalizability of fiber orientation estimation in the brain.
  • To address the limitations of existing methods in terms of cross-scanner consistency.

Main Methods:

  • Developed a new neural network architecture, the null space deep network (NSDN), designed to learn from both ground-truth data (ex-vivo DW-MRI and histology) and repeated scan data.
  • Trained the NSDN using ex-vivo DW-MRI and histology from squirrel monkey brains, augmented with repeated human scan data from two different MRI scanners.
  • Validated the NSDN's performance on a held-out set of histology voxels, comparing its accuracy and reproducibility against traditional methods like CSD and other deep learning approaches.

Main Results:

  • The NSDN demonstrated significant improvements in absolute performance compared to CSD (3.87%) and a recent deep learning method (1.42%) when evaluated against histology.
  • Reproducibility was substantially enhanced by the NSDN, outperforming CSD by 21.19% and a recent deep learning approach by 10.09% on paired scan data.
  • The NSDN showed improved generalizability to an unseen in vivo human scanner, outperforming CSD by 16.08% and a recent deep learning approach by 10.41%.

Conclusions:

  • Data-driven approaches, exemplified by the NSDN, offer a more reproducible, informative, and precise method for local fiber reconstruction in the brain.
  • The proposed NSDN provides a novel and practical solution for improving the reliability of DW-MRI-based fiber tractography.
  • This study highlights the potential of advanced machine learning techniques to overcome limitations in neuroimaging reproducibility and accuracy.