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 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 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 Multilevel Deficiency of White Matter Connectivity Networks in Alzheimer’s Disease: A Diffusion MRI Study with DTI and HARDI Models

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Tao Wang ◽  
Feng Shi ◽  
Yan Jin ◽  
Pew-Thian Yap ◽  
Chong-Yaw Wee ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
Small World ◽  
Clustering Coefficient ◽  
Elderly Subjects ◽  
High Angular Resolution ◽  
Structural Networks

Alzheimer’s disease (AD) is the most common form of dementia in elderly people. It is an irreversible and progressive brain disease. In this paper, we utilized diffusion-weighted imaging (DWI) to detect abnormal topological organization of white matter (WM) structural networks. We compared the differences between WM connectivity characteristics at global, regional, and local levels in 26 patients with probable AD and 16 normal control (NC) elderly subjects, using connectivity networks constructed with the diffusion tensor imaging (DTI) model and the high angular resolution diffusion imaging (HARDI) model, respectively. At the global level, we found that the WM structural networks of both AD and NC groups had a small-world topology; however, the AD group showed a significant decrease in both global and local efficiency, but an increase in clustering coefficient and the average shortest path length. We further found that the AD patients had significantly decreased nodal efficiency at the regional level, as well as weaker connections in multiple local cortical and subcortical regions, such as precuneus, temporal lobe, hippocampus, and thalamus. The HARDI model was found to be more advantageous than the DTI model, as it was more sensitive to the deficiencies in AD at all of the three levels.

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 Q -ball imaging of macaque white matter architecture

2005 ◽  
Vol 360 (1457) ◽  
pp. 869-879 ◽  
Author(s):  
David S Tuch ◽  
Jonathan J Wisco ◽  
Mark H Khachaturian ◽  
Leeland B Ekstrom ◽  
Rolf Kötter ◽  
...  
Keyword(s):  
White Matter ◽  
Magnetic Resonance ◽  
Diffusion Imaging ◽  
Large Body ◽  
Macaque Monkey ◽  
High Angular Resolution ◽  
Imaging Method ◽  
Diffusion Tractography ◽  
Axonal Projections ◽  
Histological Validation

Diffusion-weighted magnetic resonance imaging holds substantial promise as a technique for non-invasive imaging of white matter (WM) axonal projections. For diffusion imaging to be capable of providing new insight into the connectional neuroanatomy of the human brain, it will be necessary to histologically validate the technique against established tracer methods such as horseradish peroxidase and biocytin histochemistry. The macaque monkey provides an ideal model for histological validation of the diffusion imaging method due to the phylogenetic proximity between humans and macaques, the gyrencephalic structure of the macaque cortex, the large body of knowledge on the neuroanatomic connectivity of the macaque brain and the ability to use comparable magnetic resonance acquisition protocols in both species. Recently, it has been shown that high angular resolution diffusion imaging (HARDI) can resolve multiple axon orientations within an individual imaging voxel in human WM. This capability promises to boost the accuracy of tract reconstructions from diffusion imaging. If the macaque is to serve as a model for histological validation of the diffusion tractography method, it will be necessary to show that HARDI can also resolve intravoxel architecture in macaque WM. The present study therefore sought to test whether the technique can resolve intravoxel structure in macaque WM. Using a HARDI method called q -ball imaging (QBI) it was possible to resolve composite intravoxel architecture in a number of anatomic regions. QBI resolved intravoxel structure in, for example, the dorsolateral convexity, the pontine decussation, the pulvinar and temporal subcortical WM. The paper concludes by reviewing remaining challenges for the diffusion tractography project.

scholarly journals Anatomy and white matter connections of the orbitofrontal gyrus

2018 ◽  
Vol 128 (6) ◽  
pp. 1865-1872 ◽  
Author(s):  
Joshua D. Burks ◽  
Andrew K. Conner ◽  
Phillip A. Bonney ◽  
Chad A. Glenn ◽  
Cordell M. Baker ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Tensor ◽  
Diffusion Imaging ◽  
Internal Capsule ◽  
Ground Truth ◽  
Outcome Evaluation ◽  
Anterior Cingulate ◽  
Anatomical Dissection ◽  
Visual Spatial ◽  
Human Connectome Project

OBJECTIVEThe orbitofrontal cortex (OFC) is understood to have a role in outcome evaluation and risk assessment and is commonly involved with infiltrative tumors. A detailed understanding of the exact location and nature of associated white matter tracts could significantly improve postoperative morbidity related to declining capacity. Through diffusion tensor imaging–based fiber tracking validated by gross anatomical dissection as ground truth, the authors have characterized these connections based on relationships to other well-known structures.METHODSDiffusion imaging from the Human Connectome Project for 10 healthy adult controls was used for tractography analysis. The OFC was evaluated as a whole based on connectivity with other regions. All OFC tracts were mapped in both hemispheres, and a lateralization index was calculated with resultant tract volumes. Ten postmortem dissections were then performed using a modified Klingler technique to demonstrate the location of major tracts.RESULTSThe authors identified 3 major connections of the OFC: a bundle to the thalamus and anterior cingulate gyrus, passing inferior to the caudate and medial to the vertical fibers of the thalamic projections; a bundle to the brainstem, traveling lateral to the caudate and medial to the internal capsule; and radiations to the parietal and occipital lobes traveling with the inferior fronto-occipital fasciculus.CONCLUSIONSThe OFC is an important center for processing visual, spatial, and emotional information. Subtle differences in executive functioning following surgery for frontal lobe tumors may be better understood in the context of the fiber-bundle anatomy highlighted by this study.

scholarly journals Evaluating the performance of 3-tissue constrained spherical deconvolution pipelines for within-tumor tractography

2019 ◽  
Author(s):  
Hannelore Aerts ◽  
Thijs Dhollander ◽  
Daniele Marinazzo
Keyword(s):  
White Matter ◽  
Tumor Tissue ◽  
Diffusion Tensor ◽  
Orientation Distribution ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Fiber Orientation Distribution ◽  
Tissue Compartments ◽  
Diffusion Tensor Imaging Dti ◽  
Single Shell

AbstractThe use of diffusion MRI (dMRI) for assisting in the planning of neurosurgery has become increasingly common practice, allowing to non-invasively map white matter pathways via tractography techniques. Limitations of earlier pipelines based on the diffusion tensor imaging (DTI) model have since been revealed and improvements were made possible by constrained spherical deconvolution (CSD) pipelines. CSD allows to resolve a full white matter (WM) fiber orientation distribution (FOD), which can describe so-called “crossing fibers”: complex local geometries of WM tracts, which DTI fails to model. This was found to have a profound impact on tractography results, with substantial implications for presurgical decision making and planning. More recently, CSD itself has been extended to allow for modeling of other tissue compartments in addition to the WM FOD, typically resulting in a 3-tissue CSD model. It seems likely this may improve the capability to resolve WM FODs in the presence of infiltrating tumor tissue. In this work, we evaluated the performance of 3-tissue CSD pipelines, with a focus on within-tumor tractography. We found that a technique named single-shell 3-tissue CSD (SS3T-CSD) successfully allowed tractography within infiltrating gliomas, without increasing existing single-shell dMRI acquisition requirements.

scholarly journals Early white matter development is abnormal in tuberous sclerosis complex patients who develop autism spectrum disorder

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna K. Prohl ◽  
◽  
Benoit Scherrer ◽  
Xavier Tomas-Fernandez ◽  
Peter E. Davis ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
White Matter ◽  
Tuberous Sclerosis ◽  
Tuberous Sclerosis Complex ◽  
Neural Circuits ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fiber Bundles ◽  
The Brain

Abstract Background Autism spectrum disorder (ASD) is prevalent in tuberous sclerosis complex (TSC), occurring in approximately 50% of patients, and is hypothesized to be caused by disruption of neural circuits early in life. Tubers, or benign hamartomas distributed stochastically throughout the brain, are the most conspicuous of TSC neuropathology, but have not been consistently associated with ASD. Widespread neuropathology of the white matter, including deficits in myelination, neuronal migration, and axon formation, exist and may underlie ASD in TSC. We sought to identify the neural circuits associated with ASD in TSC by identifying white matter microstructural deficits in a prospectively recruited, longitudinally studied cohort of TSC infants. Methods TSC infants were recruited within their first year of life and longitudinally imaged at time of recruitment, 12 months of age, and at 24 months of age. Autism was diagnosed at 24 months of age with the ADOS-2. There were 108 subjects (62 TSC-ASD, 55% male; 46 TSC+ASD, 52% male) with at least one MRI and a 24-month ADOS, for a total of 187 MRI scans analyzed (109 TSC-ASD; 78 TSC+ASD). Diffusion tensor imaging properties of multiple white matter fiber bundles were sampled using a region of interest approach. Linear mixed effects modeling was performed to test the hypothesis that infants who develop ASD exhibit poor white matter microstructural integrity over the first 2 years of life compared to those who do not develop ASD. Results Subjects with TSC and ASD exhibited reduced fractional anisotropy in 9 of 17 white matter regions, sampled from the arcuate fasciculus, cingulum, corpus callosum, anterior limbs of the internal capsule, and the sagittal stratum, over the first 2 years of life compared to TSC subjects without ASD. Mean diffusivity trajectories did not differ between groups. Conclusions Underconnectivity across multiple white matter fiber bundles develops over the first 2 years of life in subjects with TSC and ASD. Future studies examining brain-behavior relationships are needed to determine how variation in the brain structure is associated with ASD symptoms.

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