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.

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 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.

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 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 White Matter Fiber Tractography Using Nonuniform Rational B-Splines Curve Fitting

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Zhanxiong Wu ◽  
Dongnan Wu ◽  
Dong Xu
Keyword(s):  
Curve Fitting ◽  
Diffusion Imaging ◽  
Orientation Distribution ◽  
High Angular Resolution ◽  
Fiber Tractography ◽  
Nurbs Curve ◽  
B Splines ◽  
Neural Fiber ◽  
Clinical Measures ◽  
Anatomical Connection

The study of neural connectivity has grown rapidly in the past decade. Revealing brain anatomical connection improves not only clinical measures but also cognition understanding. In order to achieve this goal, we have to track neural fiber pathways first. Aiming to estimate 3D fiber pathways more accurately from orientation distribution function (ODF) fields, we presented a novel tracking method based on nonuniform rational B-splines (NURBS) curve fitting. First, we constructed ODF fields from high angular resolution diffusion imaging (HARDI) datasets using diffusion orientation transform (DOT) method. Second, under the angular and length constraints, the consecutive diffusion directions were extracted along each fiber pathway starting from a seed voxel. Finally, after the coordinates of the control points and their corresponding weights were determined, NURBS curve fitting was employed to track fiber pathways. The performance of the proposal has been evaluated on the tractometer phantom and real brain datasets. Based on several measure metrics, the resulting fiber pathways show promising anatomic consistency.

scholarly journals Validation of q -ball imaging with a diffusion fibre-crossing phantom on a clinical scanner

2005 ◽  
Vol 360 (1457) ◽  
pp. 881-891 ◽  
Author(s):  
Muriel Perrin ◽  
Cyril Poupon ◽  
Bernard Rieul ◽  
Patrick Leroux ◽  
André Constantinesco ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
Ex Vivo ◽  
Diffusion Imaging ◽  
Orientation Distribution ◽  
Single Fibre ◽  
High Angular Resolution ◽  
Tensor Model ◽  
Structural Anisotropy ◽  
Brain White Matter ◽  
Clinical Scanner

Magnetic resonance (MR) diffusion imaging provides a valuable tool used for inferring structural anisotropy of brain white matter connectivity from diffusion tensor imaging. Recently, several high angular resolution diffusion models were introduced in order to overcome the inadequacy of the tensor model for describing fibre crossing within a single voxel. Among them, q -ball imaging (QBI), inherited from the q -space method, relies on a spherical Radon transform providing a direct relationship between the diffusion-weighted MR signal and the orientation distribution function (ODF). Experimental validation of these methods in a model system is necessary to determine the accuracy of the methods and to optimize them. A diffusion phantom made up of two textile rayon fibre (comparable in diameter to axons) bundles, crossing at 90°, was designed and dedicated to ex vivo q -ball validation on a clinical scanner. Normalized ODFs were calculated inside regions of interest corresponding to monomodal and bimodal configurations of underlying structures. Three-dimensional renderings of ODFs revealed monomodal shapes for voxels containing single-fibre population and bimodal patterns for voxels located within the crossing area. Principal orientations were estimated from ODFs and were compared with a priori structural fibre directions, validating efficiency of QBI for depicting fibre crossing. In the homogeneous regions, QBI detected the fibre angle with an accuracy of 19° and in the fibre-crossing region with an accuracy of 30°.

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