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 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 A PSO-Powell Hybrid Method to Extract Fiber Orientations from ODF

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Zhanxiong Wu ◽  
Xiaohui Yu ◽  
Yang Liu ◽  
Ming Hong
Keyword(s):  
Hybrid Method ◽  
Diffusion Imaging ◽  
Pso Algorithm ◽  
Orientation Distribution ◽  
Estimation Accuracy ◽  
High Angular Resolution ◽  
Sliding Windows ◽  
Key Factor ◽  
Searching Ability

High angular resolution diffusion imaging (HARDI) has opened up new perspectives for the delineation of crossing and branching fiber pathways by orientation distribution function (ODF). The fiber orientations contained in an imaging voxel are the key factor in tractography. To extract real fiber orientations from ODF, a hybrid method is proposed for computing the principal directions of ODF by combining the variation of Particle Swarm Optimization (PSO) algorithm with the modified Powell algorithm. This method is comprised of the global searching ability of PSO and the powerful local optimizing of Powell search. This combination can guarantee finding all the diffusion directions without applying sliding windows and improve the accuracy and efficiency. The proposed approach was evaluated on simulated crossing-fiber datasets, Tractometer, and in vivo datasets. The results show that this method could correctly identify fiber directions under a range of noise levels. This method was compared with the state-of-the-art methods, such as modified Powell, ball-stick model, and diffusion decomposition, showing that it outperformed them. As to the multimodal voxels where different fiber populations exist, the proposed approach allows us to improve the estimation accuracy of fiber orientations from ODF. It can play a significant role in the nerve fiber tracking.

Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing

2020 ◽  
Vol 85 (3) ◽  
pp. 1397-1413
Author(s):  
Maarten Naeyaert ◽  
Jan Aelterman ◽  
Johan Van Audekerke ◽  
Vladimir Golkov ◽  
Daniel Cremers ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Angular Resolution ◽  
Spin Echo ◽  
Diffusion Imaging ◽  
Fast Spin Echo ◽  
High Angular Resolution ◽  
Isotropic Resolution ◽  
Fast Spin

scholarly journals Optimal Short-Time Acquisition Schemes in High Angular Resolution Diffusion-Weighted Imaging

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
V. Prčkovska ◽  
H. C. Achterberg ◽  
M. Bastiani ◽  
P. Pullens ◽  
E. Balmashnova ◽  
...  
Keyword(s):  
High Performance ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
High Angular Resolution ◽  
Brain Scans ◽  
Reconstruction Methods ◽  
Pdf Estimation ◽  
Non Gaussian ◽  
Short Time

This work investigates the possibilities of applying high-angular-resolution-diffusion-imaging- (HARDI-) based methods in a clinical setting by investigating the performance of non-Gaussian diffusion probability density function (PDF) estimation for a range ofb-values and diffusion gradient direction tables. It does so at realistic SNR levels achievable in limited time on a high-performance 3T system for the whole human brainin vivo. We use both computational simulations andin vivobrain scans to quantify the angular resolution of two selected reconstruction methods: Q-ball imaging and the diffusion orientation transform. We propose a new analytical solution to the ODF derived from the DOT. Both techniques are analytical decomposition approaches that require identical acquisition and modest postprocessing times and, given the proposed modifications of the DOT, can be analyzed in a similar fashion. We find that an optimal HARDI protocol given a stringent time constraint (<10 min) combines a moderateb-value (around 2000 s/mm2) with a relatively low number of acquired directions (>48). Our findings generalize to other methods and additional improvements in MR acquisition techniques.

scholarly journals Fiber Visualization with LIC Maps Using Multidirectional Anisotropic Glyph Samples

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Mark Höller ◽  
Kay-M. Otto ◽  
Uwe Klose ◽  
Samuel Groeschel ◽  
Hans-H. Ehricke
Keyword(s):  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
Three Dimensional ◽  
Orientation Distribution ◽  
Brain Regions ◽  
High Angular Resolution ◽  
Tumor Patient ◽  
Integral Convolution ◽  
Fiber Orientation Distribution

Line integral convolution (LIC) is used as a texture-based technique in computer graphics for flow field visualization. In diffusion tensor imaging (DTI), LIC bridges the gap between local approaches, for example directionally encoded fractional anisotropy mapping and techniques analyzing global relationships between brain regions, such as streamline tracking. In this paper an advancement of a previously published multikernel LIC approach for high angular resolution diffusion imaging visualization is proposed: a novel sampling scheme is developed to generate anisotropic glyph samples that can be used as an input pattern to the LIC algorithm. Multicylindrical glyph samples, derived from fiber orientation distribution (FOD) functions, are used, which provide a method for anisotropic packing along integrated fiber lines controlled by a uniform random algorithm. This allows two- and three-dimensional LIC maps to be generated, depicting fiber structures with excellent contrast, even in regions of crossing and branching fibers. Furthermore, a color-coding model for the fused visualization of slices from T1 datasets together with directionally encoded LIC maps is proposed. The methodology is evaluated by a simulation study with a synthetic dataset, representing crossing and bending fibers. In addition, results fromin vivostudies with a healthy volunteer and a brain tumor patient are presented to demonstrate the method's practicality.

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