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Coregistration of DKI MRI data with high b-values
Krejčí, Ladislav ; Gajdoš, Martin (referee) ; Vojtíšek, Lubomír (advisor)
Magnetic resonance (MRI), DKI data, DTI data, CHARMED b-value, coregistration, voxels, image processing, registration methods, registration software
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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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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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Tool for analysis of subject's movements in functional magnetic resonance measurements.
Šejnoha, Radim ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
This diploma thesis deals with an analysis of subject’s movement during measurements with funcional magnetic resonance imaging (fMRI). It focuses on methods of a movement artifacts detection and their removal in fMRI images. Thesis deals with metrics which are used for the movement rate of measured subjects evaluation. Metrics and a correction of movement are implemented into the programme in MATLAB. Comparison of subjects suffering from Parkinson’s disease with a group of healthy control was carried out. Tresholds of individual metrics were suggested and a criterion for the removal of subjects with high movement rate was determined.
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Functional connectivity and brain structure assessment in patients at risk of synucleinopathies
Klobušiaková, Patrícia ; Gajdoš, Martin (referee) ; Mekyska, Jiří (advisor)
Synucleinopathy is a neurodegenerative disorder characterized by the presence of pathological protein -synuclein in neurons. So far, treatment that could heal or permanently stop this disease is not known. The aim of this work is to identify prodromal stages of synucleinopathies using functional connectivity processed applying graph metrics and assessing cortical thickness and subcortical structures volumes from magnetic resonance imaging data, and to verify specificity and sensitivity of combinations of parameters that sufficiently differentiate patients in risk of synucleinopathies. To accomplish this goal, we collected data from patients in the risk of synucleinopathy (preDLB, n = 27) and healthy controls (HC, n = 28). We found reduced volume of right pallidum and increased hippocampal volume to cortical volume ratio, increased normalised clustering coefficient and higher modularity in the preDLB group in comparison to HC. These four parameters were modeled using machine learning. The resulting model differentiated preDLB and HC with balanced accuracy of 88 %, specificity of 89 % and sensitivity of 86 %. The findings of this thesis can serve as the basis for further studies searching for specific MRI markers of prodromal stage of synucleinopathy that could be targeted with therapy in the future.
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Influence of parcellation atlas on quality of classification in patients with neurodegenerative dissease
Montilla, Michaela ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
The aim of the thesis is to define the dependency of the classification of patients affected by neurodegenerative diseases on the choice of the parcellation atlas. Part of this thesis is the application of the functional connectivity analysis and the calculation of graph metrics according to the method published by Olaf Sporns and Mikail Rubinov [1] on fMRI data measured at CEITEC MU. The application is preceded by the theoretical research of parcellation atlases for brain segmentation from fMRI frames and the research of mathematical methods for classification as well as classifiers of neurodegenerative diseases. The first chapters of the thesis brings a theoretical basis of knowledge from the field of magnetic and functional magnetic resonance imaging. The physical principles of the method, the conditions and the course of acquisition of image data are defined. The third chapter summarizes the graph metrics used in the diploma thesis for analyzing and classifying graphs. The paper presents a brief overview of the brain segmentation methods, with the focuse on the atlas-based segmentation. After a theoretical research of functional connectivity methods and mathematical classification methods, the findings were used for segmentation, calculation of graph metrics and for classification of fMRI images obtained from 96 subjects into the one of two classes using Binary classifications by support vector machines and linear discriminatory analysis. The data classified in this study was measured on patiens with Parkinson’s disease (PD), Alzheimer’s disease (AD), Mild cognitive impairment (MCI), a combination of PD and MCI and subjects belonging to the control group of healthy individuals. For pre-processing and analysis, the MATLAB environment, the SPM12 toolbox and The Brain Connectivity Toolbox were used.
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Visualization and export outputs from functional magnetic resonance imaging
Přibyl, Jakub ; Gajdoš, Martin (referee) ; Slavíček, Tomáš (advisor)
Thesis discusses the principles and methodology for measuring functional magnetic resonance imaging (fMRI), basically the origin and use of BOLD signal types used experiments. Further attention is paid fMRI data processing and statistical analysis. Subsequent chapters are devoted to a brief description of the most common software tools used to analyze data from fMRI. The main section was to create a program in MATLAB with a detailed graphic user interface for easy visualization and export output from analyzes of fMRI data. The second half is devoted to describing the program developer and graphic user interface, including key functionality. The final section describes the application program with real data from clinical studies of dynamic connectivity and use in an international project APGem.
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Analysis of electroencephalograms
Gajdoš, Martin ; Rozman, Jiří (referee) ; Kolářová, Jana (advisor)
Electroencephalography is sensitive diagnostic method for measuring electric potentials generated by brain. In this project are described the properties of the EEG signal and methods of EEG measuring, processing and evaluating. Also the noise sources and methods for noise removing are described. The project deals in the second part with detection of drowsiness and microsleep from driver’s EEG. At first theoretically, then is shown the practical measurement of EEG on volunteers. During the measurement was intention to induce drowsiness and microsleep. Finally is described the processing of measured EEG signals and the results are visualized.
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Coregistration of DKI MRI data with high b-values
Krejčí, Ladislav ; Gajdoš, Martin (referee) ; Vojtíšek, Lubomír (advisor)
This semestral thesis deals with several options of medical images registration. Basic properties of DKI data are presented in the begining. In the following chapters it is focused on methods, which are removing motion distortion and geometrical inaccuracies. These methods are oversampling, transformation smoothing and optimalization. Software doing registration itself is also covered in this thesis. Methods of comparing registration success rates are described in the end.
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