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 Experiment Videos

Model-based variational smoothing and segmentation for diffusion tensor imaging in the brain.

Mukund Desai1, David N Kennedy, Rami Mangoubi

  • 1Control and Information Systems Division, C.S. Draper Laboratory, Cambridge, MA 02139, USA. mdesai@draper.com

Neuroinformatics
|September 1, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

ABCD-ReproNim: An educational program for responsible and reproducible analyses of ABCD data.

Developmental cognitive neuroscience·2026
Same author

Distinct cortical excitability and connectivity profiles within the human SMA complex.

bioRxiv : the preprint server for biology·2026
Same author

Electroconvulsive therapy is associated with a decrease in anhedonia and axial diffusivity in the medial forebrain bundle in major depressive disorder.

Journal of affective disorders·2025
Same author

AGFS-tractometry: A novel atlas-guided fine-scale tractometry approach for enhanced along-tract group statistical comparison using diffusion MRI tractography.

Medical image analysis·2025
Same author

A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography.

Human brain mapping·2025
Same author

Representative scale-invariant characteristics of male and female brains in magnetic resonance images.

Neuroimage. Reports·2025
Same journal

Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers.

Neuroinformatics·2026
Same journal

CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training.

Neuroinformatics·2026
Same journal

Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.

Neuroinformatics·2026
Same journal

HESREN: A Derivative-Informed Reservoir Framework for Detecting Transient Neural Events and Windowless Estimation of Dynamic Functional Connectivity.

Neuroinformatics·2026
Same journal

Computational Morphometry of Peripheral Nerves: A Pipeline Perspective on Reproducibility and Generalization.

Neuroinformatics·2026
Same journal

Multimodal Branched Transport Infers Anatomically Aligned Brain Reaction Maps.

Neuroinformatics·2026
See all related articles

This study introduces a unified variational method for smoothing and segmenting brain diffusion tensor images. It enhances white matter visualization and improves the delineation of brain fiber systems.

Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Diffusion Tensor Imaging (DTI) is crucial for visualizing white matter structure.
  • Traditional DTI analysis often faces challenges with noise and accurate segmentation.
  • Extracting detailed information about brain fiber tracts requires advanced processing techniques.

Purpose of the Study:

  • To develop and apply a unified variational approach for simultaneous smoothing and segmentation of DTI data.
  • To extract detailed brain structure information by analyzing tensor attributes.
  • To enable robust visualization of white matter structures and fiber systems.

Main Methods:

  • A unified variational framework for smoothing and segmentation of diffusion tensor image data.

Related Experiment Videos

  • Utilizing user-selected tensor attributes (e.g., fractional anisotropy, directional components).
  • Simultaneous denoising and edge detection within white matter regions.

    • Main Results:

    • The method produces smoothed, scale-invariant representations of tensor data.
    • It effectively segments and denoises white matter, preserving edge features.
    • Comparison of three data models demonstrates varying abilities to resolve white matter structure.
    • Improved brain image quality, both qualitatively and quantitatively, shown even with added noise.

    Conclusions:

    • The variational approach offers a robust method for enhancing DTI data quality and structural detail.
    • Simultaneous segmentation and smoothing preserve crucial edge information for better fiber tract delineation.
    • The framework facilitates improved visualization and analysis of association, projection, and commissural fiber systems.