scholarly journals Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging

2003 ◽  
Vol 50 (5) ◽  
pp. 955-965 ◽  
Author(s):  
Evren Özarslan ◽  
Thomas H. Mareci
Keyword(s):  
Diffusion Tensor Imaging ◽  
Angular Resolution ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
High Angular Resolution

scholarly journals Dimensionality Reduction of Diffusion MRI Measures for Improved Tractometry of the Human Brain

2019 ◽  
Author(s):  
Maxime Chamberland ◽  
Erika P. Raven ◽  
Sila Genc ◽  
Kate Duffy ◽  
Maxime Descoteaux ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Angular Resolution ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
High Angular Resolution ◽  
Typically Developing ◽  
Typically Developing Children ◽  
Tissue Microstructure ◽  
Age Related ◽  
Diffusion Tensor Imaging Dti

AbstractVarious diffusion MRI measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different diffusion measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. In this work, we first demonstrate redundancies in the amount of information captured by 10 diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) measures. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in commonly-used DTI and HARDI measures profiled along 22 brain pathways extracted from typically developing children aged 8 - 18 years (n = 36). The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. Our results also suggest that HARDI measures are more sensitive at detecting age-related changes in tissue microstructure than DTI measures.

scholarly journals Structural abnormalities in thalamo-prefrontal tracks revealed by high angular resolution diffusion imaging predict working memory scores in concussed children

2020 ◽  
Vol 225 (1) ◽  
pp. 441-459 ◽  
Author(s):  
Guido I. Guberman ◽  
Jean-Christophe Houde ◽  
Alain Ptito ◽  
Isabelle Gagnon ◽  
Maxime Descoteaux
Keyword(s):  
Working Memory ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
High Angular Resolution ◽  
Structural Abnormalities

Inference of multiple fiber orientations in high angular resolution diffusion imaging

2005 ◽  
Vol 54 (6) ◽  
pp. 1480-1489 ◽  
Author(s):  
Tim Hosey ◽  
Guy Williams ◽  
Richard Ansorge
Keyword(s):  
Angular Resolution ◽  
Diffusion Imaging ◽  
High Angular Resolution

scholarly journals Computational Representation of White Matter Fiber Orientations

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Adelino R. Ferreira da Silva
Keyword(s):  
White Matter ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
Real Data ◽  
Orientation Distribution ◽  
Directional Data ◽  
High Angular Resolution ◽  
Clustering Data ◽  
Von Mises ◽  
Computational Representation

We present a new methodology based on directional data clustering to represent white matter fiber orientations in magnetic resonance analyses for high angular resolution diffusion imaging. A probabilistic methodology is proposed for estimating intravoxel principal fiber directions, based on clustering directional data arising from orientation distribution function (ODF) profiles. ODF reconstructions are used to estimate intravoxel fiber directions using mixtures of von Mises-Fisher distributions. The method focuses on clustering data on the unit sphere, where complexity arises from representing ODF profiles as directional data. The proposed method is validated on synthetic simulations, as well as on a real data experiment. Based on experiments, we show that by clustering profile data using mixtures of von Mises-Fisher distributions it is possible to estimate multiple fiber configurations in a more robust manner than currently used approaches, without recourse to regularization or sharpening procedures. The method holds promise to support robust tractographic methodologies and to build realistic models of white matter tracts in the human brain.

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