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Simulation of synthetic diffusion tensor data
Labudová, Kristýna ; Mézl, Martin (referee) ; Labounek, René (advisor)
This work deals with different approaches to imaging of diffusion intensity with magnetic resonance. Individual approaches are described and compared. Gaussian model for approximation of diffusion profile is analysed and mathematically determined in details. The next part of this work concerns about process of simulation synthetic diffusion tensor data, adding noise to data and estimation of diffusion tensor from noisy data. Estimation’s accuracy is rated according to deviation of fractional anisotropy of estimated and original tensor and also according to deviation of the main eigenvectors of both tensors. Accuracy of the estimation is evaluated automatically with the programme. There is realization of graphical interface for simulation as well as for automatical evaluation of results described in details. At the end of this work all results are processed and commented and there is also recommendation for optimal adjustment of the data acquisition. 120 gradient directions are the most optimal of all analysed direction. It provides sufficient accuracy of results with optimal time of data acquisition which is suitable for clinical praxis.
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Simulations of synthetic diffusion MRI data based on Brownian motion
Valla, Radek ; Mikl, Michal (referee) ; Labounek, René (advisor)
This master thesis focuses on dMRI (diffusion magnetic resonance imaging) and its dependance on diffusion in human brain tissue. It is described how to retrieve an image from gained data and its properties, advantages and disadvantages. It mentions problem in detecting kissing fibres due to its similarity with crossing fibres. Design of mathematical models of axons is decribed and suggested measurement to detect difference in signals for kissing and crossing fibres. It describes new simulator of diffusion-weighted MRI (dMRI) data which is able to generate it based on random walk algorithm with geometrical constraints not only for crossing fiber geometry, but also as o novelty for bending and kissing fiber geometries. This study contains results of simulations and disscusion about their usefulness with suggestions for simulator improvement. Simulated dMRI data shows significant difference in certain gradients. Data reconstruction shows, that these reults cannot be reconstructed into the same geometry as it was simulated for.
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Simulation of synthetic diffusion tensor data
Labudová, Kristýna ; Mézl, Martin (referee) ; Labounek, René (advisor)
This work deals with different approaches to imaging of diffusion intensity with magnetic resonance. Individual approaches are described and compared. Gaussian model for approximation of diffusion profile is analysed and mathematically determined in details. The next part of this work concerns about process of simulation synthetic diffusion tensor data, adding noise to data and estimation of diffusion tensor from noisy data. Estimation’s accuracy is rated according to deviation of fractional anisotropy of estimated and original tensor and also according to deviation of the main eigenvectors of both tensors. Accuracy of the estimation is evaluated automatically with the programme. There is realization of graphical interface for simulation as well as for automatical evaluation of results described in details. At the end of this work all results are processed and commented and there is also recommendation for optimal adjustment of the data acquisition. 120 gradient directions are the most optimal of all analysed direction. It provides sufficient accuracy of results with optimal time of data acquisition which is suitable for clinical praxis.
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|
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Simulations of synthetic diffusion MRI data based on Brownian motion
Valla, Radek ; Mikl, Michal (referee) ; Labounek, René (advisor)
This master thesis focuses on dMRI (diffusion magnetic resonance imaging) and its dependance on diffusion in human brain tissue. It is described how to retrieve an image from gained data and its properties, advantages and disadvantages. It mentions problem in detecting kissing fibres due to its similarity with crossing fibres. Design of mathematical models of axons is decribed and suggested measurement to detect difference in signals for kissing and crossing fibres. It describes new simulator of diffusion-weighted MRI (dMRI) data which is able to generate it based on random walk algorithm with geometrical constraints not only for crossing fiber geometry, but also as o novelty for bending and kissing fiber geometries. This study contains results of simulations and disscusion about their usefulness with suggestions for simulator improvement. Simulated dMRI data shows significant difference in certain gradients. Data reconstruction shows, that these reults cannot be reconstructed into the same geometry as it was simulated for.
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