scholarly journals Spatial normalization of the fiber orientation distribution based on high angular resolution diffusion imaging data

2009 ◽  
Vol 61 (6) ◽  
pp. 1520-1527 ◽  
Author(s):  
Xin Hong ◽  
Lori R. Arlinghaus ◽  
Adam W. Anderson
Keyword(s):  
Fiber Orientation ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
Orientation Distribution ◽  
High Angular Resolution ◽  
Imaging Data ◽  
Spatial Normalization ◽  
Fiber Orientation Distribution

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 FOD-Net: A Deep Learning Method for Fiber Orientation Distribution Angular Super Resolution

2021 ◽  
Author(s):  
Rui Zeng ◽  
Jinglei Lv ◽  
He Wang ◽  
Luping Zhou ◽  
Michael Barnett ◽  
...  
Keyword(s):  
Deep Learning ◽  
Angular Resolution ◽  
Super Resolution ◽  
Orientation Distribution ◽  
Quality Data ◽  
High Angular Resolution ◽  
Clinical Protocols ◽  
High Quality ◽  
Human Connectome Project ◽  
Fiber Orientation Distribution

ABSTRACTMapping the human connectome using fibre-tracking permits the study of brain connectivity and yields new insights into neuroscience. However, reliable connectome reconstruction using diffusion magnetic resonance imaging (dMRI) data acquired by widely available clinical protocols remains challenging, thus limiting the connectome/tractography clinical applications. Here we develop fibre orientation distribution (FOD) network (FOD-Net), a deep-learning-based framework for FOD angular super-resolution. Our method enhances the angular resolution of FOD images computed from common clinical-quality dMRI data, to obtain FODs with quality comparable to those produced from advanced research scanners. Super-resolved FOD images enable superior tractography and structural connectome reconstruction from clinical protocols. The method was trained and tested with high-quality data from the Human Connectome Project (HCP) and further validated with a local clinical 3.0T scanner. Using this method, we improve the angular resolution of FOD images acquired with typical single-shell low-angular-resolution dMRI data (e.g., 32 directions, b=1000 s/mm2) to approximate the quality of FODs derived from time-consuming, multi-shell high-angular-resolution dMRI research protocols. We also demonstrate tractography improvement, removing spurious connections and bridging missing connections. We further demonstrate that connectomes reconstructed by super-resolved FOD achieve comparable results to those obtained with more advanced dMRI acquisition protocols, on both HCP and clinical 3T data. Advances in deep-learning approaches used in FOD-Net facilitate the generation of high quality tractography/connectome analysis from existing clinical MRI environments.

DETERMINATION OF FIBER ORIENTATION IN HIGH ANGULAR RESOLUTION DIFFUSION IMAGING USING SPHERICAL HARMONICS AND PARTICLE SWARM OPTIMIZATION

2021 ◽  
Author(s):  
M. Hashemi Kamangar ◽  
M. R. Karami Mollaei ◽  
Reza Ghaderi
Keyword(s):  
Particle Swarm Optimization ◽  
Fiber Orientation ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
Synthetic Data ◽  
Delta Function ◽  
Dirac Delta Function ◽  
High Angular Resolution ◽  
Swarm Optimization ◽  
Dirac Delta

The fiber directions in High Angular Resolution Diffusion Imaging (HARDI) with low fractional anisotropy or low Signal to Noise Ratio (SNR) cannot be estimated accurately. In this paper, the fiber directions are estimated using Particle Swarm Optimization and Spherical Deconvolution (PSO-SD). Fiber orientation is modeled as a Dirac delta function in [Formula: see text]. The Spherical Harmonic Coefficients (SHC) of the Dirac delta function in the [Formula: see text] direction are obtained using the rotational harmonic matrix and the SHC of the Dirac delta function in the [Formula: see text]-axis. The PSO-SD method is used to determine ([Formula: see text]). We generated noise-free synthetic data for isotropic regions (FA varied from 0.1 to 0.8) and synthetic data with two crossing fibers for anisotropic regions with SNRs of 20, 15, 10 and 5 (FA [Formula: see text] 0.78). In the noise-free signal (FA [Formula: see text] 0.3), the Success Ratio (SR) and Mean Difference Angle (MDA) of the PSO-SD method were 1∘ and 9.48∘, respectively. In the noisy signal (FA [Formula: see text] 0.78, SNR [Formula: see text] 10, crossing angle [Formula: see text] 40), the SR and MDA of PSO-SD (with [Formula: see text]) were 0.46∘ and 12.3∘, respectively. The PSO-SD method can estimate fiber directions in HARDI with low fractional anisotropy and low SNR. Moreover, it has a higher SR and lower MDA in comparison with those of the super-CSD method.

scholarly journals Using High Angular Resolution Diffusion Imaging Data to Discriminate Cortical Regions

PLoS ONE ◽  
2013 ◽  
Vol 8 (5) ◽  
pp. e63842 ◽  
Author(s):  
Zoltan Nagy ◽  
Daniel C. Alexander ◽  
David L. Thomas ◽  
Nikolaus Weiskopf ◽  
Martin I. Sereno
Keyword(s):  
Angular Resolution ◽  
Diffusion Imaging ◽  
High Angular Resolution ◽  
Imaging Data ◽  
Cortical Regions
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