Abstract:
Proton magnetic resonance spectroscopic imaging (1H-MRSI) provides a non invasive, spatially resolved evaluation of brain metabolism. In the first part of this study, an open-source data analysis software, which includes modules for visualization of raw 1H-MRSI data and LCModel outputs, chemical shift correction, tissue fraction calculation, metabolite map production, and registration onto standard MNI152 brain atlas while providing automatic spectral quality control, is presented. In the second part of this study, we investigated metabolic changes of mild cognitive impairment in Parkinson’s disease (PD-MCI) using 1H-MRSI data. This could be summarized mainly as ’posterior cortical metabolic changes’ related with cognitive dysfunction. In the last part of this thesis, the spatial resolution of 1H-MRSI images were increased using super resolution convolutional neural networks (SRCNN) and enhanced deep residual networks for single image super-resolution (EDSR) models trained with the anatomical MR images. Our results indicated that deep learning based super resolution models would contribute to reconstructing higher resolution 1H-MRSI. This thesis contributed to the literature in terms of developing Oryx-MRSI, which provides an unprecedented detailed data analysis pipeline for 1H-MRSI, identifying metabolic correlates of PD MCI, which might aid the clinicians for the diagnosis of MCI, and implementing deep learning based super resolution approaches that might increase the spatial resolution of 1H-MRSI.|Keywords : Parkinson’s disease, mild cognitive impairment, proton magnetic reso nance spectroscopic imaging, super resolution, deep learning, convolutional neural networks, open-source software.
