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

Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

901
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
901
Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

Radiological Investigation II: MRI and Ventilation Perfusion Scan

623
Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
623

You might also read

Related Articles

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

Sort by
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

Professional Identity Formation in the model curriculum of human medicine in Oldenburg - a longitudinal approach.

GMS journal for medical education·2026
Same author

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

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

Correction: Neural network assisted annotation and analysis tool to study in-vivo foveolar cone photoreceptor topography.

Scientific reports·2025
Same author

Neural network assisted annotation and analysis tool to study in-vivo foveolar cone photoreceptor topography.

Scientific reports·2025
Same author

Structural White Matter Correlates of the Crowding Effect: Insights From a Tractography Study of the Arcuate Fasciculus Post-Hemispherotomy.

Human brain mapping·2025
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Feb 6, 2026

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

DT-MRI Streamsurfaces Revisited.

Michael Ankele, Thomas Schultz

    IEEE Transactions on Visualization and Computer Graphics
    |August 22, 2018
    PubMed
    Summary
    This summary is machine-generated.

    Diffusion tensor MRI (DT-MRI) streamsurfaces visualize brain structure. A new test shows these surfaces are not always mathematically valid in real brain data, requiring new visualization methods.

    More Related Videos

    Optogenetic Functional MRI
    06:06

    Optogenetic Functional MRI

    Published on: April 19, 2016

    15.4K
    The Microscopic Transcanal Approach in Stapes Surgery Revisited
    07:35

    The Microscopic Transcanal Approach in Stapes Surgery Revisited

    Published on: February 16, 2022

    2.9K

    Related Experiment Videos

    Last Updated: Feb 6, 2026

    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
    Optogenetic Functional MRI
    06:06

    Optogenetic Functional MRI

    Published on: April 19, 2016

    15.4K
    The Microscopic Transcanal Approach in Stapes Surgery Revisited
    07:35

    The Microscopic Transcanal Approach in Stapes Surgery Revisited

    Published on: February 16, 2022

    2.9K

    Area of Science:

    • Medical Imaging
    • Neuroscience
    • Computer Vision

    Background:

    • Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is used to visualize white matter tracts.
    • Streamsurfaces are a visualization technique for DT-MRI data, assuming eigenvector field integrability.
    • The integrability of eigenvector fields in real DT-MRI data has not been systematically evaluated.

    Purpose of the Study:

    • To introduce and validate a computational test for DT-MRI streamsurface integrability.
    • To assess the degree to which streamsurface integrability holds in human brain datasets.
    • To propose an improved method for DT-MRI streamsurface visualization.

    Main Methods:

    • Developed and implemented an efficiently computable test for eigenvector field integrability.
    • Validated the test using simulated data with known integrable and non-integrable configurations.
    • Applied the integrability test to whole-brain DT-MRI datasets from 15 healthy subjects.
    • Introduced a novel patch-based approach for streamsurface visualization to mitigate artifacts.

    Main Results:

    • The integrability test successfully distinguished between integrable and non-integrable fields in simulations.
    • DT-MRI streamsurface integrability was found to be approximately satisfied in significant portions of the brain.
    • Non-integrable regions were identified, even in areas exhibiting planar behavior.
    • The proposed patch-based visualization method reduced artifacts and improved sampling of streamsurfaces.

    Conclusions:

    • Explicitly testing for local integrability is crucial for reliable DT-MRI streamsurface extraction.
    • Current streamsurface methods may produce artifacts due to violated integrability assumptions in certain brain regions.
    • The novel patch-based approach offers a more robust method for visualizing DT-MRI streamsurfaces, enhancing anatomical representation.