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.

scholarly journals In vivo characterization of the connectivity and subcomponents of the human globus pallidus

2015 ◽  
Author(s):  
Patrick Beukema ◽  
Timothy Verstynen ◽  
Fang-Cheng Yeh
Keyword(s):  
Angular Resolution ◽  
Diffusion Imaging ◽  
Orientation Distribution ◽  
High Angular Resolution ◽  
Fiber Direction ◽  
Degree Of Anisotropy ◽  
In Vivo Characterization ◽  
Diffusion Properties

Projections from the substantia nigra and striatum traverse through the pallidum on the way to their targets. To date, in vivo characterization of these pathways remains elusive. Here we used high angular resolution diffusion imaging (N=138) to study the characteristics and structural subcompartments of the human pallidum. Our results show that the diffusion orientation distribution at the pallidum is asymmetrically oriented in a dorsolateral direction, consistent with the orientation of underlying fiber systems. Furthermore, compared to the outer pallidal segment, the internal segment has more peaks in the orientation distribution function and stronger anisotropy in the primary fiber direction, consistent with known cellular differences between the underlying nuclei. These differences in orientation, complexity, and degree of anisotropy are sufficiently robust to automatically segment the pallidal nuclei using diffusion properties. Thus the gray matter diffusion signal can be useful as an in vivo measure of the collective nigrostriatal and striatonigral pathways.

scholarly journals Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Demian Wassermann ◽  
Maxime Descoteaux ◽  
Rachid Deriche
Keyword(s):  
White Matter ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
Orientation Distribution ◽  
Fiber Bundles ◽  
High Angular Resolution ◽  
Complex Data ◽  
Diffusion Maps ◽  
Clustering Method ◽  
Number Of Clusters

White matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a diffusion maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles. It is well known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent high-angular resolution diffusion imaging (HARDI) such as Q-Ball imaging (QBI) has been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations. In this paper, we use a spherical harmonic ODF representation as input to the diffusion maps clustering method. We first show the advantage of using diffusion maps clustering over classical methods such as N-Cuts and Laplacian eigenmaps. In particular, our ODF diffusion maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation, and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptive scale-space parameter. We also show that our ODF diffusion maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters. Finally, we show results on a real-brain dataset where we segment well-known fiber bundles.

scholarly journals High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity

2002 ◽  
Vol 48 (4) ◽  
pp. 577-582 ◽  
Author(s):  
David S. Tuch ◽  
Timothy G. Reese ◽  
Mette R. Wiegell ◽  
Nikos Makris ◽  
John W. Belliveau ◽  
...  
Keyword(s):  
White Matter ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
High Angular Resolution

scholarly journals Motion Detection in Diffusion MRI via Online ODF Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Emmanuel Caruyer ◽  
Iman Aganj ◽  
Christophe Lenglet ◽  
Guillermo Sapiro ◽  
Rachid Deriche
Keyword(s):  
Kalman Filter ◽  
Diffusion Mri ◽  
Angular Resolution ◽  
Real Data ◽  
Orientation Distribution ◽  
High Angular Resolution ◽  
Ratio Test ◽  
Small Motion ◽  
Scan Time ◽  
Robustness To Noise

The acquisition of high angular resolution diffusion MRI is particularly long and subject motion can become an issue. The orientation distribution function (ODF) can be reconstructed online incrementally from diffusion-weighted MRI with a Kalman filtering framework. This online reconstruction provides real-time feedback throughout the acquisition process. In this article, the Kalman filter is first adapted to the reconstruction of the ODF in constant solid angle. Then, a method called STAR (STatistical Analysis of Residuals) is presented and applied to the online detection of motion in high angular resolution diffusion images. Compared to existing techniques, this method is image based and is built on top of a Kalman filter. Therefore, it introduces no additional scan time and does not require additional hardware. The performance of STAR is tested on simulated and real data and compared to the classical generalized likelihood ratio test. Successful detection of small motion is reported (rotation under 2°) with no delay and robustness to noise.

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