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Improvement of the Biomedical Image Reconstruction Methodology Based on Impedance Tomography
Kořínková, Ksenia ; Bartušek, Karel (oponent) ; Bachorec, Tibor (oponent) ; Dědková, Jarmila (vedoucí práce)
The present theoretical thesis discusses the improvement and related research of algorithms for the imaging of the internal structure of conductive objects, biological tissues and organs in particular, via electrical impedance tomography (EIT). Within the thesis, the theoretical framework of EIT is formulated, together with a survey of approaches towards implementing the given technique. More concretely, in this context, algorithms for the solution of the inverse problem are proposed and researched; these algorithms ensure effective reconstruction of the spatial distribution of the electrical properties of the examined object and facilitate the imaging of such properties. The main idea of the algorithm improvement based on the deterministic approach lies in introducing additional techniques, namely, the level set or the fuzzy filter methods. Furthermore, a procedure for the 2-D reconstruction of conductivity distribution utilizing one component of the measured magnetic field, or the z-component of magnetic flux density, is presented. Numerical models for imaging the admittivity (or conductivity) distribution of a biological tissue were created to facilitate the implementation and testing of the algorithms. The results obtained via the basic application of the improved image reconstruction algorithms are discussed and compared.
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DEEP LEARNING FOR SINGLE-VOXEL AND MULTIDIMENSIONAL MR-SPECTROSCOPIC SIGNAL QUANTIFICATION, AND ITS COMPARISON WITH NONLINEAR LEAST-SQUARES FITTING
Shamaei, Amir Mohammad ; Latta,, Peter (oponent) ; Kozubek, Michal (oponent) ; Jiřík, Radovan (vedoucí práce)
Preprocessing, analysis, and quantification of Magnetic resonance spectroscopy (MRS) signals are required for obtaining the metabolite concentrations of the tissue under investigation. However, a fast, accurate, and efficient post-acquisition workflow (preprocessing, analysis, and quantification) of MRS is challenging. This thesis introduces novel deep learning (DL)-based approaches for preprocessing, analysis, and quantification of MRS data. The proposed methods achieved the objectives of robust data preprocessing, fast and efficient MR spectra quantification, in-vivo concentration quantification, and the uncertainty estimation of quantification. The results showed that the proposed approaches significantly improved the speed of MRS signal preprocessing and quantification in a self-supervised manner. Our proposed methods showed comparable results with the traditional methods in terms of accuracy. Furthermore, a standard data format was introduced to facilitate data sharing among research groups for artificial intelligence applications. The findings of this study suggest that the proposed DL-based approaches have the potential to improve the accuracy and efficiency of MRS for medical diagnosis. The dissertation is structured into four parts: an introduction, a review of state-of-the-art research, a summary of the aims and objectives, and a collection of publications that showcase the author's contribution to the field of DL applications in MRS.
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Improvement of the Biomedical Image Reconstruction Methodology Based on Impedance Tomography
Kořínková, Ksenia ; Bartušek, Karel (oponent) ; Bachorec, Tibor (oponent) ; Dědková, Jarmila (vedoucí práce)
The present theoretical thesis discusses the improvement and related research of algorithms for the imaging of the internal structure of conductive objects, biological tissues and organs in particular, via electrical impedance tomography (EIT). Within the thesis, the theoretical framework of EIT is formulated, together with a survey of approaches towards implementing the given technique. More concretely, in this context, algorithms for the solution of the inverse problem are proposed and researched; these algorithms ensure effective reconstruction of the spatial distribution of the electrical properties of the examined object and facilitate the imaging of such properties. The main idea of the algorithm improvement based on the deterministic approach lies in introducing additional techniques, namely, the level set or the fuzzy filter methods. Furthermore, a procedure for the 2-D reconstruction of conductivity distribution utilizing one component of the measured magnetic field, or the z-component of magnetic flux density, is presented. Numerical models for imaging the admittivity (or conductivity) distribution of a biological tissue were created to facilitate the implementation and testing of the algorithms. The results obtained via the basic application of the improved image reconstruction algorithms are discussed and compared.
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