Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry.

Alonso Ramirez-Manzanares, Mariano Rivera, Baba C Vemuri, Paul Carney, Thomas Mareci

August 2007 IEEE Trans Med Imaging

Synopsis of Social media discussions

Discussions highlight the method’s ability to accurately interpret complex fiber crossings, with phrases like 'game-changer' and 'more efficient' reflecting excitement about its applications. Participants often reference its improved robustness and fewer required images, indicating a recognition of its practical relevance and promising future impact.

A
Agreement
 Moderate agreement

Most discussions acknowledge the significance of the new diffusion basis functions method, with some expressing strong approval of its efficiency and robustness.

I
Interest
 Moderate level of interest

Participants show a moderate level of interest, often mentioning how the technique could advance neuroimaging and voxel analysis.

E
Engagement
 Moderate level of engagement

The comments suggest that users are engaging by exploring potential applications and technical details, but not deeply debating or critiquing the method.

I
Impact
 Moderate level of impact

There is a shared view that this research could have a meaningful influence on future brain imaging studies and clinical diagnostics.

Social Mentions

YouTube

3 Videos

Twitter

1 Posts

Metrics

Video Views

966

Total Likes

5

Extended Reach

1,366

Social Features

4

Timeline: Posts about article

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Posts referencing the article

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  • GeoScience.net
    @GeoSciences (Twitter)

    Diffusion basis functions decomposition for estimating white matter intravoxel fiber geometry https://t.co/T37ljW8ecF
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    November 22, 2022

Abstract Synopsis

  • The paper introduces a new way to identify the geometry of neuronal fiber tracts in a single voxel using diffusion weighted MRI data, focusing on situations with single or multiple fibers.
  • It leverages discrete diffusion basis functions to recover detailed intravoxel information, particularly in areas where fibers cross or split, resulting in a representation of fiber geometry as a mixture of Gaussians.
  • The technique is efficient, requiring fewer diffusion images and less acquisition time, and it shows robustness even when dealing with more than two fibers, while two algorithmic solutions are proposed for implementation.