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Influence of region coordinates selection on dynamic causal modelling results
Klímová, Jana ; Mikl, Michal (referee) ; Lamoš, Martin (advisor)
This thesis deals with functional magnetic resonance imaging (fMRI), in particular with dynamic causal modelling (DCM) as one of the methods for effective brain connectivity analysis. It has been studied the effect of signal coordinates selection, which was used as an input of DCM analysis, on its results based on simulated data testing. For this purpose, a data simulator has been created and described in this thesis. Furthermore, the methodology of testing the influence of the coordinates selection on DCM results has been reported. The coordinates shift rate has been simulated by adding appropriate levels of various types of noise signals to the BOLD signal. Consequently, the data has been analyzed by DCM. The program has been supplemented by a graphical user interface. To determine behaviour of the model, Monte Carlo simulations have been applied. Results in the form of dependence of incorrectly estimated connections between brain areas on the level of the noise signals have been processed and discussed.
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Reduction of movement artifacts in BOLD fMRI data using rejection of motion-corrupted scans
Svatoň, Jan ; Gajdoš, Martin (referee) ; Mikl, Michal (advisor)
Tato bakalářská práce ze zprvu zabývá elementárními principy magnetické rezonance a zdrojů šumu a artefaktů v datech. Dále práce podrobněji pojednává o pohybovém artefaktu a navrhuje dvě vhodné metody pro lokalizaci a odstranění pohybem postižených skenů BOLD fMRI dat. Metody jsou poté implementovány v prostředí MATLAB a otestovány na vhodných datasetech poskytnutých Laboratoří multimodálního a funkčního zobrazování, CEITEC MU. Nakonec jsou prezentovány a vyhodnoceny výsledky zároveň s doporučením pro vhodný způsob eliminace pohybového artefaktu v datech.
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Noise and artifact suppression in fMRI data based on multi-echo data and independent component analysis
Pospíšil, Jan ; Gajdoš, Martin (referee) ; Mikl, Michal (advisor)
The main task of this work is to design an algorithm for suppressing unwanted noise and artifacts in fMRI data using the analysis of independent components and multi-echo data. The theoretical part deals with the basic principles of magnetic resonance, including construction and image data processing. The practical part presents a pilot design of a method inspired by a professional publication in the Matlab software environment, where this design is subsequently tested on real fMRI data provided by the Laboratory of Multimodal and Functional Imaging, CEITEC MU.
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Advanced Methods of Perfusion Analysis in MRI
Macíček, Ondřej ; Frollo, Ivan (referee) ; Mikl, Michal (referee) ; Jiřík, Radovan (advisor)
This dissertation deals with quantitative perfusion analysis of MRI contrast-enhanced image time sequences. It focuses on two so far separately used methods -- Dynamic contrast-enhanced MRI (DCE-MRI) and Dynamic susceptibility contrast MRI (DSC-MRI). The common problem of such perfusion analyses is the unreliability of perfusion parameters estimation. This penalizes usage of these unique techniques on a regular basis. The presented methods are intended to improve these drawbacks, especially the problems with quantification in DSC in case of contrast agent extravasation and instability of the deconvolution process in DCE using advanced pharmacokinetic models. There are a few approaches in literature combining DCE and DSC to estimate new parameters of the examined tissue, namely the relaxivity of the vascular and of the interstitial space. Originally, in this scheme, the 2CXM DCE model was used. Here various models for DCE analysis are tested keeping in mind the DCE-DSC combination. The ATH model was found to perform better in this setting compared to 2CXM. Finally, the ATH model was used in alternating DCE-DSC optimization algorithm and then in a truly fully simultaneous DCE-DSC. The processing was tested using simulated and in-vivo data. According to the results, the proposed simultaneous algorithm performs better in comparison with sequential DCE-DSC, unleashing full potential of perfusion analysis using MRI.
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Processing of MREG MRI data
Lampert, Frederik ; Mikl, Michal (referee) ; Gajdoš, Martin (advisor)
MR-encephalography (MREG) is an innovative method of ultrafast magnetic resonance imaging. Most of the publications about this method are concerning about acquisition and reconstruction of raw data. Studies dedicated to standardization of preprocessing MREG data have not been published yet, which led to motivation of creating this bachelor thesis. The main goal of this thesis is to set an optimal way of preprocessing MREG data, which could be advised for future studies utilizing this method. The main goal of this work was divided into several subgoals, consisting of making a literary review, implementation of general method for data preprocessing and suggesting an alternative ways of data preprocessing and their implementation into MATLAB programming language. Suggested ways of data preprocessing were evaluated by created criteria, described in this work. Results of the evaluation were discussed and interpreted by graphs. Based on the results of the evaluation, an optimal way for preprocessing data was set. It consists of movement and geometric distortion correction accomplished by SPM Realign & UNWARP function, spatial normalisation to EPI MNI template and spatial smoothing by Gaussian kernel.
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Comparison and assessment of single-echo and multi-echo BOLD fMRI acquisition
Kovářová, Anežka ; Jiřík, Radovan (referee) ; Mikl, Michal (advisor)
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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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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Impact of Inaccuracy in fMRI Experimental Stimulation
Mikl, Michal ; Kremláček,, Jan (referee) ; Michálek, Jiří (referee) ; Drastich, Aleš (advisor)
Aim of this work is to study the impact of inaccuracy in execution of required task (inaccuracy in subject’s behavioral response to experimental stimulation) by person who undergoes fMRI examination. The work is solved in several stages. First, theoretical analysis of inaccuracy in fMRI experiment was performed, and simulations with synthetic data were created. Several variables in general linear model and t-statistics were followed. We found that estimated effect size depends linearly on covariance between the corresponding columns of X and D matrices or their linear combination. The component of residual variance caused by inaccuracy is negligible at real-life noise levels. In such case, moreover, the dependence of t-statistics on inaccuracy becomes linear. Next, our theoretical results (dependencies/characteristics of variables) were verified using real data. All results were confirmed. Last, I focused on possible practical use of the uncovered characteristics and dependencies. Optimization of experimental design with respect to inaccuracy, correction of inaccurate results and reliability of inaccurate results are introduced and discussed. Especially, the calculation of maps of maximal tolerable inaccuracy can be useful to find robust or weak (tending to be not detected or to be significantly different from accurate value) activation in real fMRI experiments.
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Processing of MREG MRI data
Lampert, Frederik ; Mikl, Michal (referee) ; Gajdoš, Martin (advisor)
MR-encephalography (MREG) is an innovative method of ultrafast magnetic resonance imaging. Most of the publications about this method are concerning about acquisition and reconstruction of raw data. Studies dedicated to standardization of preprocessing MREG data have not been published yet, which led to motivation of creating this bachelor thesis. The main goal of this thesis is to set an optimal way of preprocessing MREG data, which could be advised for future studies utilizing this method. The main goal of this work was divided into several subgoals, consisting of making a literary review, implementation of general method for data preprocessing and suggesting an alternative ways of data preprocessing and their implementation into MATLAB programming language. Suggested ways of data preprocessing were evaluated by created criteria, described in this work. Results of the evaluation were discussed and interpreted by graphs. Based on the results of the evaluation, an optimal way for preprocessing data was set. It consists of movement and geometric distortion correction accomplished by SPM Realign & UNWARP function, spatial normalisation to EPI MNI template and spatial smoothing by Gaussian kernel.
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