A localized Richardson-Lucy algorithm for fiber orientation estimation in high angular resolution diffusion imaging

2015 ◽  
Vol 42 (5) ◽  
pp. 2524-2539 ◽  
Author(s):  
Xiaozheng Liu ◽  
Zhenming Yuan ◽  
Zhongwei Guo ◽  
Dongrong Xu
Keyword(s):  
Fiber Orientation ◽  
Angular Resolution ◽  
Diffusion Imaging ◽  
High Angular Resolution ◽  
Orientation Estimation

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.

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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