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Design of computer tomograph
Bojková, Eva ; Lehocký, Peter (referee) ; Zvonek, Miroslav (advisor)
The graduation thesis is engaged in the design of computed tomography (CT). It is a piece of medical equipment which helps to define the diagnosis of a patient. The first part of the project is a research into contemporary stage and production of CT systems. Consequently the new design of computed tomography is designed in a way to maximise ergonomical, technical and aesthetic quality.
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Preparation of nanoparticles and their use as contrast agents for in vivo imaging.
Odehnalová, Nikola ; Kratochvílová, Irena (referee) ; Turánek,, Jaroslav (advisor)
This diploma thesis deals with the optimalization of synthesis of gold nanoparticles and their surface modification allowing their use as contrast agents for in vivo imaging by CT. Gold nanoparticles were prepared by the Turkevich method and characterized by TEM, DLS, MADLS and UV -Vis. Their surface was functionalized with polyethylene glycol containing a thiol group forming a bond with the Au atoms in the surface of gold nanoparticles. The terminal end of the polymer was methylated or containing an aminooxy group forming an orthogonal bond with hyaluronic acid using click-chemistry. The eligibility for in vivo application of the prepared nanoparticles was verified with stability and cytotoxicity tests. The nanoparticles modified by methylated polyethyleneglycol were injected intravenously into a mouse and their application potential as contrast agents were verified by CT.
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Precise segmentation of image data
Svoboda, Jan ; Marcoň, Petr (referee) ; Mikulka, Jan (advisor)
The concern of this thesis is a development of an extension module for 3D Slicer platform. The core of the module is an implementation of a Support Vector Machines classifier, which is used for segmentation of the vertebral column image data provided by the University Hospital Brno. One of the goals of the thesis was resampling and registration of these image sequences. CT volumes provided solid contrast and were used as a reference for gaining properly segmented groups of vertebrae. Due to the low quality of the MRI volumes image data, segmentation of MRI images was not completely succesful. The extension module scripted in Python language can be seen as a tool and can be used in the future for different datasets.
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Use of Retomo software for computational modeling in biomechanics
Filková, Alena ; Lisický, Ondřej (referee) ; Marcián, Petr (advisor)
This bachelor thesis deals with the issue of musculoskeletal biomechanics. More precisely, of creating a geometry model based on CT data using the RETOMO program and then a computational model. The introductory part is devoted to a brief explanation of the concepts of computed tomography and finite element methods. The following chapter provides a search of programs used to create models of geometry from CT data in biomechanics. Furthermore, the work deals with the description of selected functionalities and user environment of the RETOMO program, with the greatest emphasis on the creation of a geometry model from CT data. During the implementation of the bachelor's thesis, two models of geometry were created in the analysed program, using different segmentation functions, which were then compared with each other and then compared with a model of geometry created in another program. The GOM Inspect program was used to compare the geometry deviations of individual models. Finally, computational models were created using the CATIA program and then deformation and stress analysis was performed using the ANSYS program. The results of individual solutions were analysed, compared and evaluated.
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Convolutional neural networks for identification of axial 2D slices in CT data
Vavřinová, Pavlína ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the classification of axial 2D slices in CT patient’s data into six categories. The sphere of convolutional neural networks was used for this purpose. For a better understanding of this issue, the basics of neural networks and then the principles of deep learning including convolutional neural networks are explained at first. The AlexNet network was specifically selected for the intention of this identification, and it was tested on the created data set after being adaptated. The overall classification success rate was 86% ,after the final adjustments, a slight improvement was achieved and the identification success rate was 87%.
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Time development analysis of treated lesion in spinal CT data
Nohel, Michal ; Jan, Jiří (referee) ; Jakubíček, Roman (advisor)
This diploma thesis is focused on time-development analysis of treated lesion in CT data. The theoretical part of the thesis deals with the anatomy, physiology, and pathophysiology of the spine and vertebral bodies. It further describes diagnostic and therapeutic options for the detection and treatment of spinal lesions. It contains an overview of the current state of usage of time-development analysis in oncology. The problems of the available databases are discussed and new databases are created for subsequent analysis. Futhermore, the methodology of time-development analysis according to the shape characterization and the size of the vertebral involvement is proposed. The proposed methodological approaches to feature extraction are applied to the created databases. Their choice and suitability is discussed, including their potential for possible usege in clinical practice of monitoring the development and derivation of characteristic dependences of features on the patient's prognosis.
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Methods of Detection, Segmentation and Classification of Difficult to Define Bone Tumor Lesions in 3D CT Data
Chmelík, Jiří ; Flusser,, Jan (referee) ; Kozubek, Michal (referee) ; Jan, Jiří (advisor)
The aim of this work was the development of algorithms for detection segmentation and classification of difficult to define bone metastatic cancerous lesions from spinal CT image data. For this purpose, the patient database was created and annotated by medical experts. Successively, three methods were proposed and developed; the first of them is based on the reworking and combination of methods developed during the preceding project phase, the second method is a fast variant based on the fuzzy k-means cluster analysis, the third method uses modern machine learning algorithms, specifically deep learning of convolutional neural networks. Further, an approach that elaborates the results by a subsequent random forest based meta-analysis of detected lesion candidates was proposed. The achieved results were objectively evaluated and compared with results achieved by algorithms published by other authors. The evaluation was done by two objective methodologies, technical voxel-based and clinical object-based ones. The achieved results were subsequently evaluated and discussed.
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Segmentation of cortical parts of vertebrae
Janštová, Michaela ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
This thesis deals with a segmentation of cortical parts of vertebrae from CT image datas in programming software called MATALB. Issues about segmentation techniques are described, especially „level-set” method and its modification DRLSE. This method was chosen because of informations from articles published in spcialized publications and also thanks to its plentiful usage and satisfactory results. In the end of this paper is designed method tested on real CT datas.
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Detection and segmentation of lumbar vertebrae in 3D CT data
Nemček, Jakub ; Kolář, Radim (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the detection and the segmentation of lumbar vertebrae in CT image datas. The described detection method is based on the use of a trained SVM classificator and histograms of oriented gradients as the image features. The detection method is applied on two-dimensional sagital slices of the CT image. The segmentation method is implemented as triangular mesh model deformation of models, that are obtained from averaged vertebrae in real CT datas. The first part of the thesis describes essential theoretical knowledge about the anatomy of the axial skeleton, computer tomography, image processing methods and about the detection and segmentation issues. The second part contains the algorithms realisation description, the evaluation and the discussion of the results. Applications of the algorithms in CAD systems is described at the end. The application of all of the points is done in the programming software Matlab.
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Acquisition and Detection Geometry of the CT X-ray Imaging Process
Rusz, Jakub ; Harabiš, Vratislav (referee) ; Drastich, Aleš (advisor)
The basic components of acquisition and detection geometry of a 3rd generation single slice CT scanner are described here. Then the high contrast spatial resolution of the imaging process is explained and what effects does the setting of acquisition and detection geometry changes in the limiting high contrast spatial resolution. At the end a laboratory exercise and a simulator is presented, which will show and explain these effects.
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