Human Brain Functional MRI and DTI Visualization With Virtual Reality

2011 ◽  
Author(s):  
Bin Chen ◽  
John Moreland ◽  
Jingyu Zhang
Keyword(s):  
Virtual Reality ◽  
White Matter ◽  
Human Brain ◽  
Functional Mri ◽  
Diffusion Tensor ◽  
Fiber Tractography ◽  
Functional Activities ◽  
Brain White Matter ◽  
The Brain ◽  
Activation Map

Magnetic resonance diffusion tensor imaging (DTI) and functional MRI (fMRI) are two active research areas in neuroimaging. DTI is sensitive to the anisotropic diffusion of water exerted by its macromolecular environment and has been shown useful in characterizing structures of ordered tissues such as the brain white matter, myocardium, and cartilage. The diffusion tensor provides two new types of information of water diffusion: the magnitude and the spatial orientation of water diffusivity inside the tissue. This information has been used for white matter fiber tracking to review physical neuronal pathways inside the brain. Functional MRI measures brain activations using the hemodynamic response. The statistically derived activation map corresponds to human brain functional activities caused by neuronal activities. The combination of these two methods provides a new way to understand human brain from the anatomical neuronal fiber connectivity to functional activities between different brain regions. In this study, virtual reality (VR) based MR DTI and fMRI visualization with high resolution anatomical image segmentation and registration, ROI definition and neuronal white matter fiber tractography visualization and fMRI activation map integration is proposed. Rationale and methods for producing and distributing stereoscopic videos are also discussed.

Human Brain Diffusion Tensor Imaging Visualization With Virtual Reality

2010 ◽  
Author(s):  
Bin Chen ◽  
John Moreland
Keyword(s):  
Virtual Reality ◽  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Biological Tissues ◽  
Head Tracking ◽  
Fiber Tractography ◽  
Brain White Matter ◽  
Diffusion Tensors ◽  
Visualization Techniques

Magnetic resonance diffusion tensor imaging (DTI) is sensitive to the anisotropic diffusion of water exerted by its macromolecular environment and has been shown useful in characterizing structures of ordered tissues such as the brain white matter, myocardium, and cartilage. The water diffusivity inside of biological tissues is characterized by the diffusion tensor, a rank-2 symmetrical 3×3 matrix, which consists of six independent variables. The diffusion tensor contains much information of diffusion anisotropy. However, it is difficult to perceive the characteristics of diffusion tensors by looking at the tensor elements even with the aid of traditional three dimensional visualization techniques. There is a need to fully explore the important characteristics of diffusion tensors in a straightforward and quantitative way. In this study, a virtual reality (VR) based MR DTI visualization with high resolution anatomical image segmentation and registration, ROI definition and neuronal white matter fiber tractography visualization and fMRI activation map integration is proposed. The VR application will utilize brain image visualization techniques including surface, volume, streamline and streamtube rendering, and use head tracking and wand for navigation and interaction, the application will allow the user to switch between different modalities and visualization techniques, as well making point and choose queries. The main purpose of the application is for basic research and clinical applications with quantitative and accurate measurements to depict the diffusivity or the degree of anisotropy derived from the diffusion tensor.

Microstructural Hyperelastic Characterization of Brain White Matter in Tension

2019 ◽  
Author(s):  
Mohammadreza Ramzanpour ◽  
Mohammad Hosseini-Farid ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Finite Element ◽  
White Matter ◽  
Human Brain ◽  
Volume Fraction ◽  
Fibrous Composite ◽  
Diffuse Axonal Injury ◽  
Stiffness Ratio ◽  
Fibrous Composite Material ◽  
Brain White Matter ◽  
The Brain

Abstract Axons as microstructural constituent elements of brain white matter are highly oriented in extracellular matrix (ECM) in one direction. Therefore, it is possible to model the human brain white matter as a unidirectional fibrous composite material. A micromechanical finite element model of the brain white matter is developed to indirectly measure the brain white matter constituents’ properties including axon and ECM under tensile loading. Experimental tension test on corona radiata conducted by Budday et al. 2017 [1] is used in this study and one-term Ogden hyperelastic constitutive model is applied to characterize its behavior. By the application of genetic algorithm (GA) as a black box optimization method, the Ogden hyperelastic parameters of axon and ECM minimizing the error between numerical finite element simulation and experimental results are measured. Inverse analysis is conducted on the resultant optimized parameters shows high correlation of coefficient (>99%) between the numerical and experimental data which verifies the accuracy of the optimization procedure. The volume fraction of axons in porcine brain white matter is taken to be 52.7% and the stiffness ratio of axon to ECM is perceived to be 3.0. As these values are not accurately known for human brain white matter, we study the material properties of axon and ECM for different stiffness ratio and axon volume fraction values. The results of this study helps to better understand the micromechanical structure of the brain and micro-level injuries such as diffuse axonal injury.

scholarly journals Euclidean distance as a measure to distinguish ventral and dorsal white matter connectivity in the human brain

2016 ◽  
Author(s):  
Philipp Kellmeyer ◽  
Magnus-Sebastian Vry
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Cognitive Processing ◽  
Euclidean Distance ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Large Body ◽  
Coordinate Space ◽  
Fiber Tractography

AbstractFiber tractography based on diffusion tensor imaging (DTI) has become an important research tool for investigating the anatomical connectivity between brain regions in vivo. Combining DTI with functional magnetic resonance imaging (fMRI) allows for the mapping of structural and functional architecture of large-scale networks for cognitive processing. This line of research has shown that ventral and dorsal fiber pathways subserve different aspects of bottom-up- and top-down processing in the human brain.Here, we investigate the feasibility and applicability of Euclidean distance as a simple geometric measure to differentiate ventral and dorsal long-range white matter fiber pathways tween parietal and inferior frontal cortical regions, employing a body of studies that used probabilistic tractography.We show that ventral pathways between parietal and inferior frontal cortex have on average a significantly longer Euclidean distance in 3D-coordinate space than dorsal pathways. We argue that Euclidean distance could provide a simple measure and potentially a boundary value to assess patterns of connectivity in fMRI studies. This would allow for a much broader assessment of general patterns of ventral and dorsal large-scale fiber connectivity for different cognitive operations in the large body of existing fMRI studies lacking additional DTI data.

Changes in Brain White Matter Assessed Via Textural Features Using a Neural Network

2010 ◽  
Vol 5 (2) ◽  
pp. 37-48
Author(s):  
R. Kalpana ◽  
S. Muttan ◽  
B. Agrawala
Keyword(s):  
Neural Network ◽  
White Matter ◽  
Diffusion Tensor ◽  
Age Groups ◽  
Elderly Subjects ◽  
Textural Features ◽  
Statistical Parameters ◽  
Brain White Matter ◽  
Age Related ◽  
The Brain

Diffusion Tensor Magnetic Resonance Imaging (DTMRI) has proved useful for microstructure characterization of the brain. This technique also helps determining complex connectivity of fiber tracts. The brain white matter (BMW) changes with respect to age and corresponding appearance of white-matter lesions among the brain’s message-carrying axons affects cognitive functions in old age. In this paper, the observed morphology in BWM on ageing is analyzed using statistical parameters extracted from DTMR images of different age groups. The gray level co-occurrence matrix (GLCM) obtained from the segmented images gives 14 textural features, subsets of which are adopted as the input sets in a backpropagation neural network classifier. The network is trained to predict the age based on BMW details used as the inputs. The proposed method helps in understanding the age-related changes in white matter. This is useful for the physician in understanding miscorrelation in motor activities and relevant causes in elderly subjects.

scholarly journals Life-Span Changes of the Human Brain White Matter: Diffusion Tensor Imaging (DTI) and Volumetry

2009 ◽  
Vol 20 (9) ◽  
pp. 2055-2068 ◽  
Author(s):  
L. T. Westlye ◽  
K. B. Walhovd ◽  
A. M. Dale ◽  
A. Bjornerud ◽  
P. Due-Tonnessen ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Human Brain ◽  
Life Span ◽  
Diffusion Tensor ◽  
Brain White Matter ◽  
Diffusion Tensor Imaging Dti

scholarly journals Tai Chi Training Evokes Significant Changes in Brain White Matter Network in Older Women

Healthcare ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 57 ◽  
Author(s):  
Chunlin Yue ◽  
Liye Zou ◽  
Jian Mei ◽  
Damien Moore ◽  
Fabian Herold ◽  
...  
Keyword(s):  
White Matter ◽  
Cognitive Decline ◽  
Tai Chi ◽  
Elderly Women ◽  
Diffusion Tensor ◽  
Brain White Matter ◽  
Increased Risk ◽  
Significant Difference ◽  
The Brain ◽  
Walking Group

Background: Cognitive decline is age relevant and it can start as early as middle age. The decline becomes more obvious among older adults, which is highly associated with increased risk of developing dementia (e.g., Alzheimer’s disease). White matter damage was found to be related to cognitive decline through aging. The purpose of the current study was to compare the effects of Tai Chi (TC) versus walking on the brain white matter network among Chinese elderly women. Methods: A cross-sectional study was conducted where 42 healthy elderly women were included. Tai Chi practitioners (20 females, average age: 62.9 ± 2.38 years, education level 9.05 ± 1.8 years) and the matched walking participants (22 females, average age: 63.27 ± 3.58 years, educational level: 8.86 ± 2.74 years) underwent resting-state functional magnetic resonance imaging (rsfMRI) scans. Diffusion tensor imaging (DTI) and graph theory were employed to study the data, construct the white matter matrix, and compare the brain network attributes between the two groups. Results: Results from graph-based analyses showed that the small-world attributes were higher for the TC group than for the walking group (p < 0.05, Cohen’s d = 1.534). Some effects were significant (p < 0.001) with very large effect sizes. Meanwhile, the aggregation coefficient and local efficiency attributes were also higher for the TC group than for the walking group (p > 0.05). However, no significant difference was found between the two groups in node attributes and edge analysis. Conclusion: Regular TC training is more conducive to optimize the brain functioning and networking of the elderly. The results of the current study help to identify the mechanisms underlying the cognitive protective effects of TC.

scholarly journals Automated fiber tracking of human brain white matter using diffusion tensor imaging

NeuroImage ◽  
2008 ◽  
Vol 42 (2) ◽  
pp. 771-777 ◽  
Author(s):  
Weihong Zhang ◽  
Alessandro Olivi ◽  
Samuel J. Hertig ◽  
Peter van Zijl ◽  
Susumu Mori
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Human Brain ◽  
Diffusion Tensor ◽  
Fiber Tracking ◽  
Brain White Matter
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