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°.

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 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 Acute ex vivo changes in brain white matter diffusion tensor metrics

PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0223211
Author(s):  
Matthew R. Walker ◽  
Jidan Zhong ◽  
Adam C. Waspe ◽  
Thomas Looi ◽  
Karolina Piorkowska ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Tensor ◽  
Ex Vivo ◽  
Brain White Matter

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 Age-Related Variations in Regional White Matter Volumetry and Microstructure During the Post-adolescence Period: A Cross-Sectional Study of a Cohort of 1,713 University Students

2021 ◽  
Vol 15 ◽  
Author(s):  
Ami Tsuchida ◽  
Alexandre Laurent ◽  
Fabrice Crivello ◽  
Laurent Petit ◽  
Antonietta Pepe ◽  
...  
Keyword(s):  
White Matter ◽  
University Students ◽  
Diffusion Tensor ◽  
Brain Mri ◽  
Mean Diffusivity ◽  
High Angular Resolution ◽  
White Matter Volume ◽  
Matter Volume ◽  
Brain White Matter ◽  
Age Related

Human brain white matter undergoes a protracted maturation that continues well into adulthood. Recent advances in diffusion-weighted imaging (DWI) methods allow detailed characterizations of the microstructural architecture of white matter, and they are increasingly utilized to study white matter changes during development and aging. However, relatively little is known about the late maturational changes in the microstructural architecture of white matter during post-adolescence. Here we report on regional changes in white matter volume and microstructure in young adults undergoing university-level education. As part of the MRi-Share multi-modal brain MRI database, multi-shell, high angular resolution DWI data were acquired in a unique sample of 1,713 university students aged 18–26. We assessed the age and sex dependence of diffusion metrics derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) in the white matter regions as defined in the John Hopkins University (JHU) white matter labels atlas. We demonstrate that while regional white matter volume is relatively stable over the age range of our sample, the white matter microstructural properties show clear age-related variations. Globally, it is characterized by a robust increase in neurite density index (NDI), and to a lesser extent, orientation dispersion index (ODI). These changes are accompanied by a decrease in diffusivity. In contrast, there is minimal age-related variation in fractional anisotropy. There are regional variations in these microstructural changes: some tracts, most notably cingulum bundles, show a strong age-related increase in NDI coupled with decreases in radial and mean diffusivity, while others, mainly cortico-spinal projection tracts, primarily show an ODI increase and axial diffusivity decrease. These age-related variations are not different between males and females, but males show higher NDI and ODI and lower diffusivity than females across many tracts. These findings emphasize the complexity of changes in white matter structure occurring in this critical period of late maturation in early adulthood.

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