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Related Concept Videos

Magnetic Resonance Imaging01:24

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...

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Related Experiment Video

Updated: Jun 27, 2026

Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

Mathematical methods for diffusion MRI processing.

C Lenglet1, J S W Campbell, M Descoteaux

  • 1Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota Medical School, Minneapolis, MN, USA. clenglet@umn.edu

Neuroimage
|December 10, 2008
PubMed
Summary
This summary is machine-generated.

This review covers advanced mathematical models and computational methods for processing diffusion Magnetic Resonance Images. It highlights techniques for reconstructing diffusion models, analyzing brain connectivity, and segmenting images using Diffusion Tensor Imaging and Q-Ball Imaging.

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

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Last Updated: Jun 27, 2026

Diffusion Imaging in the Rat Cervical Spinal Cord
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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

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Diffusion Magnetic Resonance Imaging (dMRI) provides insights into brain microstructure and connectivity.
  • Mathematical and computational methods are crucial for extracting meaningful information from complex dMRI data.
  • Diffusion Tensor Imaging (DTI) and Q-Ball Imaging (QBI) are key dMRI techniques with distinct data characteristics.

Purpose of the Study:

  • To provide a comprehensive review of current mathematical models and computational methods for dMRI processing.
  • To discuss state-of-the-art techniques in diffusion model reconstruction, white matter tractography, and image segmentation.
  • To highlight the application of these methods to DTI and QBI data.

Main Methods:

  • Review of mathematical frameworks for diffusion MRI signal modeling.
  • Analysis of computational algorithms for tractography and connectivity estimation.
  • Evaluation of segmentation techniques applied to diffusion MRI data.

Main Results:

  • Recent advancements in diffusion model reconstruction offer improved accuracy.
  • Sophisticated computational methods enhance the analysis of white matter connectivity.
  • Advanced segmentation techniques provide more precise delineation of brain structures.

Conclusions:

  • Mathematical and computational approaches are rapidly evolving for dMRI analysis.
  • Accurate processing of DTI and QBI data is vital for understanding brain structure and function.
  • Continued development in these methods promises further breakthroughs in neuroscience research.