Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Magnetic Resonance Imaging

Magnetic Resonance Imaging

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...

You might also read

Related Articles

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

Sort by

Same author

Shaping the future of endourology: role of education, simulation and uro-technology: a scoping review by the EAU Section of Endourology.

Current opinion in urology·2026

Same author

Subthalamic segmentations in relation to deep brain stimulation volumes in Parkinson's disease.

Acta neurochirurgica·2026

Same author

No link between piriform cortex subregion resection and seizure freedom in two cohorts with temporal lobe epilepsy.

Journal of neurology·2026

Same author

Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence.

European radiology·2026

Same author

Optimized precision oncology through implementation of a comprehensive molecular analysis pipeline - relevance for additional therapeutic options.

Cancer genetics·2026

Same author

Aromatic amino acid metabolism shapes autophagy-mediated adaptation to iron deprivation in glioblastoma cells.

Biometals : an international journal on the role of metal ions in biology, biochemistry, and medicine·2026

Same journal

Strain alignment: toward assessing mechanical plausibility of predicted displacement fields.

International journal of computer assisted radiology and surgery·2026

Same journal

Vascular geometry characterization for AI-based endovascular navigation.

International journal of computer assisted radiology and surgery·2026

Same journal

Nail It! A learning framework for autonomous surgical suturing and teleoperation on the dVRK.

International journal of computer assisted radiology and surgery·2026

Same journal

Correspondence-free local-to-global liver deformation correction via implicit neural representation and biomechanical model.

International journal of computer assisted radiology and surgery·2026

Same journal

BronchoLumen: analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy.

International journal of computer assisted radiology and surgery·2026

Same journal

Model-guided medicine for early diagnosis of transthyretin-associated cardiac amyloidosis using multimodal data integration and standardized interoperable models (the CRONOS-ATTR study).

International journal of computer assisted radiology and surgery·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 21, 2026

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

Published on: May 23, 2017

Atlas-based fiber reconstruction from diffusion tensor MRI data.

Sebastiano Barbieri¹, Jan Klein, Miriam H A Bauer

¹Fraunhofer MEVIS, Institute for Medical Image Computing, Universitätsallee 29, 28359, Bremen, Germany. sebastiano.barbieri@mevis.fraunhofer.de

International Journal of Computer Assisted Radiology and Surgery

|June 26, 2012

Summary

This summary is machine-generated.

This study introduces a novel neural fiber reconstruction method using diffusion tensor imaging. The technique enhances accuracy by reducing reliance on user-defined regions, improving robustness to image noise for better brain connectivity analysis.

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

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

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

Published on: November 8, 2012

Related Experiment Videos

Last Updated: May 21, 2026

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

Fiber Connections of the Supplementary Motor Area Revisited: Methodology of Fiber Dissection, DTI, and Three Dimensional Documentation

Published on: May 23, 2017

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

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

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

Published on: November 8, 2012

Area of Science:

Neuroimaging
Computational Neuroscience
Medical Image Analysis

Background:

Diffusion tensor imaging (DTI) is crucial for mapping white matter tracts.
Streamline tractography is a common DTI analysis method but is sensitive to user-defined regions of interest.
A need exists for more robust and less operator-dependent neural fiber reconstruction techniques.

Purpose of the Study:

To develop a novel neural fiber reconstruction method using diffusion tensor imaging.
To create a method less sensitive to user-defined regions of interest compared to streamline tractography.
To improve the reliability of white matter tract mapping.

Main Methods:

Employs a simulated annealing approach for non-rigid transformation of fiber bundles from an atlas.
Minimizes an energy functional to fit transformed fiber bundles to patient DTI data.
Utilizes a fiber atlas and diffusion tensor data for mapping.

Main Results:

Demonstrates feasibility on a diffusion tensor software phantom.
Analyzes algorithm behavior concerning image noise and iteration count.
Presents initial results from patient datasets, showing robustness to noise.

Conclusions:

The developed method effectively maps fiber bundles using diffusion tensor data.
The technique exhibits high robustness against image noise.
Future work aims to simplify inter-subject comparisons of fiber bundles for enhanced research.