|Topic:||Traktografie mozku u pacientů s Alzheimerovou chorobou|
|Supervisor:||Ing. Milan Němý|
|Announce as:||Diplomová práce, Bakalářská práce, Semestrální projekt|
|Description:||Alzheimer’s disease (AD) is the most common form of senile dementia which is generally characterized by memory loss followed by a progressive decline in other cognitive domains. Several recent studies have proposed that cognitive decline in AD is a consequence of disruptions in the structural and functional connections between brain regions. Diffusion-weighted MRI (DWI), a variant of standard anatomical magnetic resonance imaging (MRI), is sensitive to microscopic white matter injury not always detectable with standard anatomical MRI. DWI tracks anisotropic water diffusion along axons, revealing microstructural white matter bundles in the brain’s anatomical network. In this thesis, you will learn about various techniques for tracking white matter bundles using DWI data and you will implement one of the algorithms in MATLAB. Next, you will be asked to use your implementation to find several commonly identified tracts, evaluate their integrity and subsequently compare it between healthy controls and the AD group.|
|Bibliography:||1. Zhan, Liang, et al. "Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease." Frontiers in aging neuroscience 7 (2015): 48.
2. Mori, Susumu, et al. "Three‐dimensional tracking of axonal projections in the brain by magnetic resonance imaging." Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society 45.2 (1999): 265-269.
3. Behrens, Timothy EJ, et al. "Characterization and propagation of uncertainty in diffusion‐weighted MR imaging." Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 50.5 (2003): 1077-1088.
4. Lazar, Mariana, et al. "White matter tractography using diffusion tensor deflection." Human brain mapping 18.4 (2003): 306-321.