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Comparing Simultaneous Multi-slice Diffusion Acquisitions Read chapter Read first chapter

2014 | OriginalPaper | Chapter

A Novel Riemannian Metric for Geodesic Tractography in DTI

Authors : Andrea Fuster, Antonio Tristan-Vega, Tom Dela Haije, Carl-Fredrik Westin, Luc Florack

Published in: Computational Diffusion MRI and Brain Connectivity

Publisher: Springer International Publishing

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Abstract

One of the approaches in diffusion tensor imaging is to consider a Riemannian metric given by the inverse diffusion tensor. Such a metric is used for white matter tractography and connectivity analysis. We propose a modified metric tensor given by the adjugate rather than the inverse diffusion tensor. Tractography experiments on real brain diffusion data show improvement in the vicinity of isotropic diffusion regions compared to results for inverse (sharpened) diffusion tensors.

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Footnotes
1
Classification of geodesics as fibres requires additional connectivity measures [1, 16].
 
2
We use Einstein’s summation convention: \(a_{i}{b}^{i}\stackrel{\mbox{ def}}{=}\sum \limits _{i}a_{i}{b}^{i}\).
 
3
Here we consider the shortest geodesic between any given pair of points to be optimal.
 
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Metadata
Title
A Novel Riemannian Metric for Geodesic Tractography in DTI
Authors
Andrea Fuster
Antonio Tristan-Vega
Tom Dela Haije
Carl-Fredrik Westin
Luc Florack
Copyright Year
2014
Book
Computational Diffusion MRI and Brain Connectivity

Print ISBN: 978-3-319-02474-5

Electronic ISBN: 978-3-319-02475-2

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-02475-2_9

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