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

4.7K
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...
4.7K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

112
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
112
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

121
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
121

You might also read

Related Articles

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

Sort by
Same author

European expert recommendations for comprehensive pre-treatment, treatment-phase and post-treatment care of patients with metachromatic leukodystrophy treated with autologous haematopoietic stem and progenitor cell gene therapy.

European journal of paediatric neurology : EJPN : official journal of the European Paediatric Neurology Society·2026
Same author

Symptom-based rehabilitation in people with post-COVID-19 condition (RELOAD study): a randomised controlled trial.

BMJ open respiratory research·2026
Same author

Continuation or Withdrawal of Life-sustaining Therapies After Acute Anoxic Events in Childhood and Adolescence : Can Early MRI Predict Particularly Severe Outcomes?

Clinical neuroradiology·2026
Same author

Automated Deep Learning-Based Demyelination Load Segmentation in Metachromatic Leukodystrophy.

Clinical neuroradiology·2026
Same author

RapidParc: A global-context transformer for parallel, accurate, and lesion-robust tractogram parcellation.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Diagnostic Clues and Pitfalls in Pontocerebellar Hypoplasia Type 2A.

Pediatric neurology·2026

Related Experiment Video

Updated: Aug 7, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.5K

Spatially regularized low-rank tensor approximation for accurate and fast tractography.

Johannes Gruen1, Samuel Groeschel2, Thomas Schultz3

  • 1Institute for Computer Science, University of Bonn, Friedrich-Hirzebruch-Allee 8, Bonn, 53115, Germany; Bonn-Aachen International Center for Information Technology, University of Bonn, Friedrich-Hirzebruch-Allee 6, Bonn, 53115, Germany.

Neuroimage
|March 10, 2023
PubMed
Summary

Two novel spatial regularization methods enhance multi-fiber tractography stability and accuracy in diffusion MRI. These approaches improve white matter tract reconstruction, even with reduced data, and offer computational efficiency.

Keywords:
Diffusion MRIJoint tensor approximationTractographyUnscented Kalman filter

More Related Videos

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
05:41

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

694
Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery
09:53

Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery

Published on: July 5, 2021

3.7K

Related Experiment Videos

Last Updated: Aug 7, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.5K
Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
05:41

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

694
Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery
09:53

Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery

Published on: July 5, 2021

3.7K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Physics

Background:

  • Diffusion Magnetic Resonance Imaging (dMRI) tractography delineates white matter tracts in vivo.
  • Estimating secondary fiber directions is challenging due to insufficient local dMRI information.

Purpose of the Study:

  • To introduce two novel spatial regularization approaches for more stable multi-fiber tractography.
  • To improve the accuracy and efficiency of white matter tract reconstruction.

Main Methods:

  • Representing the fiber Orientation Distribution Function (fODF) as a symmetric fourth-order tensor.
  • Recovering multiple fiber orientations using low-rank approximation.
  • Developing a joint approximation over local neighborhoods and integrating low-rank approximation into the unscented Kalman filter (UKF).

Main Results:

  • Improved tractography quality on Human Connectome Project data, even with reduced measurements.
  • Increased overlap and reduced overreach on the ISMRM tractography challenge data.
  • More comprehensive reconstruction of tracts around a tumor in a clinical dataset.
  • Modified UKF significantly reduced computational effort.
  • Joint approximation with ROI-based seeding more fully recovered fiber spread.

Conclusions:

  • Both novel approaches enhance white matter tract reconstruction quality in dMRI.
  • The modified UKF offers significant computational advantages.
  • Joint approximation excels in recovering fiber spread when using ROI-based seeding.