scholarly journals QUANTITATIVE EXAMINATION OF A NOVEL CLUSTERING METHOD USING MAGNETIC RESONANCE DIFFUSION TENSOR TRACTOGRAPHY

2008 ◽  
Vol 31 (4) ◽  
pp. 24
Author(s):  
Aristotle N Voineskos ◽  
L J O’Donnell ◽  
N J Lobaugh ◽  
D Markant ◽  
M Niethammer ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
A Priori ◽  
Region Of Interest ◽  
Clustering Method ◽  
Fiber Tracts ◽  
Clustering Approach ◽  
White Matter Fiber Tracts ◽  
Highly Correlated ◽  
High Level

Introduction: MR diffusion tensor imaging (DTI) is the most powerful and currentlythe only way to visualize the organization of white matter fiber tracts in vivo. As this is a relatively newimaging technique, new tools are developed for quantifying fiber tracts, andrequire evaluation. We examined scalar indices of the diffusion tensor with two different tractography methods. We compared a novel clustering approach with a multiple region of interest (MROI) approach in a healthy and disease (schizophrenia) population. Methods: DTI images were acquired in 12 participants (n=6 patients withschizophrenia: 58 ± 12 years; n=6 controls: 57 ± 21 years) on a 1.5 Tesla GE system with diffusion gradients applied in 23 non-collinear directions, repeated three times. Tractography andfiber tract creation was performed using 3D Slicer software. Interraterreliability of the clustering approach and its similarity to the MROI methodwere evaluated. Results: The clustering approach was reliable both quantitatively and spatially (k > 0.8 for all tracts). There was high spatial(voxel-based) agreement between the clustering and MROI methods. Fractionalan isotropy and trace values were highly correlated between the clustering and MROI methods (p < 0.001 for all tracts). Discussion: Our clustering method has excellent interrater reliability and thereis a high level of agreement between our clustering method and the MROI method, both quantitatively and spatially. The clustering method is less susceptible touser bias. Moreover, not limited by a priori predictions, our clustering method may be a more robust and efficient way to identify and measure fiber tracts of interest. (colour figure available in PDF version)

scholarly journals Meyer’s Loop Anatomy Demonstrated Using Diffusion Tensor MR Imaging and Fiber Tractography at 3T

2014 ◽  
Vol 60 (5) ◽  
pp. 215-222 ◽  
Author(s):  
Cristina Goga ◽  
Zeynep Firat ◽  
Klara Brinzaniuc ◽  
Is Florian
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Fiber Tractography ◽  
Brain Images ◽  
3 Dimensional ◽  
Software Release ◽  
Magnetic Resonance Imaging Mri ◽  
Meyer’S Loop

Abstract Objective: The ultimate anatomy of the Meyer’s loop continues to elude us. Diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) may be able to demonstrate, in vivo, the anatomy of the complex network of white matter fibers surrounding the Meyer’s loop and the optic radiations. This study aims at exploring the anatomy of the Meyer’s loop by using DTI and fiber tractography. Methods: Ten healthy subjects underwent magnetic resonance imaging (MRI) with DTI at 3 T. Using a region-of-interest (ROI) based diffusion tensor imaging and fiber tracking software (Release 2.6, Achieva, Philips), sequential ROI were placed to reconstruct visual fibers and neighboring projection fibers involved in the formation of Meyer’s loop. The 3-dimensional (3D) reconstructed fibers were visualized by superimposition on 3-planar MRI brain images to enhance their precise anatomical localization and relationship with other anatomical structures. Results: Several projection fiber including the optic radiation, occipitopontine/parietopontine fibers and posterior thalamic peduncle participated in the formation of Meyer’s loop. Two patterns of angulation of the Meyer’s loop were found. Conclusions: DTI with DTT provides a complimentary, in vivo, method to study the details of the anatomy of the Meyer’s loop.

Relevant Biophysical Parameters Discrimination along Corticospinal Tract in Patients with Stroke Using Convolutional Neural Networks

2021 ◽  
Vol 51 ◽  
pp. 95-103
Author(s):  
Abderrazek Zeraii ◽  
Amine Ben Slama ◽  
Lazhar Rmili ◽  
Cyrine Drissi ◽  
Mokhtar Mars ◽  
...  
Keyword(s):  
Motor Neurons ◽  
Corticospinal Tract ◽  
Diffusion Tensor ◽  
Biophysical Parameters ◽  
Spinal Motor Neurons ◽  
Fiber Tracts ◽  
Subacute Stroke ◽  
Quantitative Metrics ◽  
White Matter Fiber Tracts

Stroke remains the leading source of long-term disability. As the only direct descending motor pathway, the corticospinal tract (CST) is the primary pathway to innervate spinal motor neurons and one of the most well studied tracts in human neuroanatomy. Its clinical significance can be demonstrated in many distinguished traumatic situations and diseases such as stroke. Along‐tract statistics analysis enables the extraction of quantitative diffusion metrics along specific white matter fiber tracts. Besides quantitative metrics derived from classical diffusion tensor imaging (DTI), such as fractional anisotropy and diffusivities. In this study, we extracted DTI derived quantitative microstructural diffusion metrics along the CST tract in patients with moderate to severe subacute stroke. Respectively DTI metric of individual patient's fiber tract was then plotted. This approach may be useful for future studies that may compare in two different time (acute and chronic). The contribution of this work presents a totally computerized method of DTI image recognition based on conventional neural network (CNN) in order to supply quantitative appraisal of clinical characteristics. The obtained results have achieved an important classification (Accuracy=94.12%) when applying the CNN. The proposed methodology enables us to assess the classification of the used DTI images database within a reduced processing time. Experimental results prove the success of the proposed rating system for a suitable analysis of microstructural diffusion when compared to previous work.

scholarly journals Integrative Structural Brain Network Analysis In Diffusion Tensor Imaging

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Node Degree ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Structural Brain Network ◽  
Structural Networks

AbstractIn diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length and FA-values into the connectivity model. Using various node-degree based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in post-institutional settings before being adopted by families in the US.

scholarly journals Motor cortex gliomas induces microstructural changes of large fiber tracts revealed by TBSS

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiangdong Wang ◽  
Chunyao Zhou ◽  
Lei Wang ◽  
Yinyan Wang ◽  
Tao Jiang
Keyword(s):  
White Matter ◽  
Motor Cortex ◽  
Diffusion Tensor ◽  
Early Stage ◽  
Mean Diffusivity ◽  
Cerebral White Matter ◽  
Control Group ◽  
Fiber Tracts ◽  
3.0 T ◽  
White Matter Fiber Tracts

Abstract Gliomas grow and invade along white matter fiber tracts. This study assessed the effects of motor cortex gliomas on the cerebral white matter fiber bundle skeleton. The motor cortex glioma group included 21 patients, and the control group comprised 14 healthy volunteers. Both groups underwent magnetic resonance imaging-based 3.0 T diffusion tensor imaging. We used tract-based spatial statistics to analyze the characteristics of white matter fiber bundles. The left and right motor cortex glioma groups were analyzed separately from the control group. Results were statistically corrected by the family-wise error rate. Compared with the controls, patients with left motor cortex gliomas exhibited significantly reduced fractional anisotropy and an increased radial diffusivity in the corpus callosum. The alterations in mean diffusivity (MD) and the axial diffusivity (AD) were widely distributed throughout the brain. Furthermore, atlas-based analysis showed elevated MD and AD in the contralateral superior fronto-occipital fasciculus. Motor cortex gliomas significantly affect white matter fiber microstructure proximal to the tumor. The range of affected white matter fibers may extend beyond the tumor-affected area. These changes are primarily related to early stage tumor invasion.

COMPREHENSIVE 3D FIBER TRACKING AS A NEW VISUALIZATION SYSTEM IN BRAIN STUDIES

2007 ◽  
Vol 07 (04) ◽  
pp. 749-765 ◽  
Author(s):  
DANMARY SANCHEZ ◽  
MALEK ADJOUADI ◽  
NOLAN R. ALTMAN ◽  
DANIEL SANCHEZ ◽  
BYRON BERNAL
Keyword(s):  
Diffusion Tensor ◽  
3D Visualization ◽  
Fiber Tracking ◽  
Brain Images ◽  
Visualization System ◽  
Fiber Tracts ◽  
Desirable Outcome ◽  
White Matter Fiber Tracts ◽  
Magnetic Resonance Imaging Mri ◽  
Using Data

Comprehensive spatial visualization of fiber tracts from all perspectives is a highly desirable outcome in brain studies. To achieve this aim, this study establishes the foundation for a new 3D visual interface that integrates Magnetic Resonance Imaging (MRI) to Diffusion Tensor Imaging (DTI). The need for such an interface is critical for understanding brain dynamics, and for providing accurate diagnosis of key brain dysfunctions, in terms of neuronal connectivity in the human brain. Two research fronts were explored: (1) the development of new image processing techniques resulting in comprehensive visualization mechanisms that accurately establish relational positioning of neuronal fiber tracts and key landmarks in semi-transparent 3D brain images, and (2) the design of key algorithms that do not tax the computational requirements of 3D rendering and feature extraction using 2D MRI and DTI frames, remaining within practical time constraints. The system was evaluated using data from thirty patients and volunteers with the Brain Institute at Miami Children's Hospital. The highly integrated and fully embedded fiber-tracking software system provides an optimal research environment for innovative visualization mechanisms of white matter fiber tracts. This 3D visualization system reached the implementation level that makes it ready for deployment at other clinical sites.

scholarly journals The Connectivity of the Human Pulvinar: A Diffusion Tensor Imaging Tractography Study

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Sandra E. Leh ◽  
M. Mallar Chakravarty ◽  
Alain Ptito
Keyword(s):  
Diffusion Tensor Imaging ◽  
Visual Processing ◽  
Animal Studies ◽  
Diffusion Tensor ◽  
Human Vision ◽  
Subcortical Structures ◽  
Fiber Tracts ◽  
Visual Spatial ◽  
Visual Areas

Previous studies in nonhuman primates and cats have shown that the pulvinar receives input from various cortical and subcortical areas involved in vision. Although the contribution of the pulvinar to human vision remains to be established, anatomical tracer and electrophysiological animal studies on cortico-pulvinar circuits suggest an important role of this structure in visual spatial attention, visual integration, and higher-order visual processing. Because methodological constraints limit investigations of the human pulvinar's function, its role could, up to now, only be inferred from animal studies. In the present study, we used an innovative imaging technique, Diffusion Tensor Imaging (DTI) tractography, to determine cortical and subcortical connections of the human pulvinar. We were able to reconstruct pulvinar fiber tracts and compare variability across subjects in vivo. Here we demonstrate that the human pulvinar is interconnected with subcortical structures (superior colliculus, thalamus, and caudate nucleus) as well as with cortical regions (primary visual areas (area 17), secondary visual areas (area 18, 19), visual inferotemporal areas (area 20), posterior parietal association areas (area 7), frontal eye fields and prefrontal areas). These results are consistent with the connectivity reported in animal anatomical studies.

scholarly journals Simultaneous Manifold Learning and Clustering: Grouping White Matter Fiber Tracts Using a Volumetric White Matter Atlas

2008 ◽  
Author(s):  
Demian Wassermann ◽  
Rachid Deriche
Keyword(s):  
White Matter ◽  
Manifold Learning ◽  
Clustering Algorithm ◽  
A Priori ◽  
Anatomical Structures ◽  
Fiber Tracts ◽  
Anatomical Knowledge ◽  
Graph Theoretical Approach ◽  
White Matter Fiber Tracts ◽  
Streamline Tractography

We propose a new clustering algorithm. This algorithm performs clustering and manifold learning simultaneously by using a graph-theoretical approach to manifold learning. We apply this algorithm in order to cluster white matter fiber tracts obtained fromDiffusion TensorMRI (DT-MRI) through streamline tractography. Our algorithm is able perform clustering of these fiber tracts incorporating information about the shape of the fiber and a priori knowledge as the probability of the fiber belonging to known anatomical structures. This anatomical knowledge is incorporated as a volumetric white matter atlas, in this case LONI’s ICBM DTI-81

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