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Related Concept Videos

Adjusting a Traverse01:12

Adjusting a Traverse

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In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
416

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Related Experiment Video

Updated: Mar 9, 2026

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
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Alignment of Tractograms As Graph Matching.

Emanuele Olivetti1, Nusrat Sharmin1, Paolo Avesani1

  • 1NeuroInformatics Laboratory, Bruno Kessler FoundationTrento, Italy; Center for Mind and Brain Sciences, University of TrentoTrento, Italy.

Frontiers in Neuroscience
|December 21, 2016
PubMed
Summary

This study introduces tractogram correspondence to map brain streamlines between subjects, improving structural connectome alignment. This graph matching approach offers superior alignment compared to traditional registration methods.

Keywords:
alignmentcombinatorial optimizationdiffusion MRIgraph matchingtractography

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Diffusion magnetic resonance imaging (dMRI) reconstructs brain white matter pathways as streamlines.
  • Tractograms, sets of streamlines, represent the brain's structural connectome.
  • Aligning tractograms across subjects is crucial for group analyses, segmentation, and atlasing.

Purpose of the Study:

  • To propose a novel method for aligning tractograms by establishing streamline-to-streamline correspondence.
  • To move beyond transformation-based registration towards a mapping approach between tractograms.
  • To leverage relational information between streamlines for optimal correspondence.

Main Methods:

  • Representing tractograms as graphs.
  • Utilizing streamline relational information (distances to other streamlines) as a basis for correspondence.
  • Employing combinatorial optimization and inexact sub-graph matching algorithms to find optimal streamline correspondences.
  • Comparing tractogram correspondence with affine and non-linear volume registration.

Main Results:

  • Tractogram correspondence, when implemented as graph matching, significantly outperforms affine registration.
  • The proposed method achieves alignment results comparable to or better than non-linear registration.
  • Demonstrated effectiveness on tractograms from the Human Connectome Project dataset.

Conclusions:

  • Tractogram correspondence offers a powerful new paradigm for aligning structural connectomes.
  • Graph matching provides an effective computational approach to solve the tractogram correspondence problem.
  • This method enhances the accuracy and reliability of cross-subject brain connectome analysis.