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This master’s thesis deals with functional magnetic resonance and monitoring of the effect of acquisition acceleration methods on the quality of functional images and observed BOLD signal. The basic principles of magnetic resonance imaging, the explanation of the specifics of functional magnetic resonance and the formation and scanning of BOLD signal are described here. Subsequently, there is the definition of fMRI experiment and description of sequences used for fMRI, focusing on aquisition acceleration techniques. The influence of sequence parameters on image quality and the data processing methods are explained aftewards. The practical part describes the parameters of used sequences, the acquisition procedure and the task for the subject during aquisition. Data from 26 healthy volunteers were obtained and analyzed afterwards. Based on this, the differencesbetween the different sequence variants were evaluated and the initial assumption that the multi-echo acquisition yields better results with faster measurements than single-echo was confirmed.

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Magnetic resonance imaging (MRI), is a noninvasive medical imaging testing techniquewhich is used to produce detailed images of internal structure of the human body, includingbones, muscles, organs, and blood vessels. MRI scanners use large magnets and radiowaves to create images of the body. Cardiac MRI scan helps doctors to detect and monitorcardiac diseases like blood clots, artery blockages, and scar tissue etc. Cardiovasculardisease is a type of disease that affects the heart or the blood vessels.This thesis aims to explore the reconstruction of accelerated cardiovascular MRI datato reconstruct under-sampled MRI data acquired after applying accelerated techniques.The focus of this research is to study and implement deep learning techniques to overcomethe aliasing artifacts caused by accelerated imaging. The results of this study will becompared with fully sampled data acquired with traditional existing techniques such asParallel Imaging (PI) and Compressed Sensing (CS).The primary findings of this study show that the proposed deep learning network caneffectively reconstruct under-sampled cardiovascular MRI data acquired using acceleratedimaging techniques. Many experiments were performed to handle 4D Flow data with limitedmemory for training the network. The network’s performance was found to be comparableto the fully sampled data acquired using traditional imaging techniques such asPI and CS. It is also important to note that this study also aimed to investigate the generalizabilityof the proposed deep learning network, specifically FlowVN, when appliedto different datasets. To explore this aspect, two different models were employed: a pretrainedmodel using previous research data and configurations, and a model trained fromscratch using CMIV data with experiments performed to address limited memory issuesassociated with 4D Flow data.

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