Computed tomography is one of the most evolving fields of modern radiology. The current CT applications permit among other things angiography, 3D image reconstructions, material decomposition and tissue characterization. CT is an important tool in the assessment of specific patient populations prior to an invasive or surgical procedure. The aim of this dissertation was to demonstrate the decisive role of CT with 3D-image reconstructions in haemodialysis patients scheduled to undergo fistulography, in patients undergoing surgical breast reconstructions with a perforator flap and in patients with complicated renal calculi scheduled to undergo percutaneous nephrolithotomy. CT Angiography with 3D image reconstructions was performed in 31 patients with failing arteriovenous fistulas and grafts, illustrating the vascular anatomy in a comprehensive manner in 93.5% of the evaluated segments and demonstrating a sensitivity of 95% compared to fistulography. In 59 mastectomy patients scheduled to undergo reconstructive breast surgery with a deep inferior epigastric perforator flap, the preoperative planning with CT Angiography with 3D image reconstructions of the anterior abdominal wall providing details of its vascular supply, reduced surgery time significantly (p< 0.001) and resulted in fewer complications. Dual Energy CT Urography with advanced image reconstructions in 31 patients with complicated renal calculi scheduled to undergo PNL, resulted in a new method of material characterisation (depicting renal calculi within excreted contrast) and in the possibility of reducing radiation dose by 28% by omitting the nonenhanced scanning phase. Detailed analysis of the changes renal calculi undergo when virtually reconstructed was performed and a comparison of renal calculi number, volume, height and attenuation between virtual nonenhanced and true nonenhanced images was undertaken. All parameters were significantly underestimated in the virtual nonenhanced images. CT with 3D-reconstructions is more than just “flashy images”. It is crucial in preoperative planning, optimizes various procedures and can reduce radiation dose.

The presented thesis discusses the basic analysis methods of arteriovelocitograms. The core of this work rests in classification of signals and contribution to possibilities of noninvasive diagnostic methods for evaluation patients with peripheral ischemic occlusive arterial disease. The classification employs multivariate statistical methods and principles of neural networks. The data processing works with an angiographic verified set of arteriovelocitogram dates. The digital subtraction angiography classified them into 3 separable classes in dependence on degree of vascular stenosis. Classification AVG signals are represented in the program by the 6 parameters that are measured on 3 different places on each patient’s leg. Evaluation of disease appeared to be a comprehensive approach at signals acquired from whole patient’s leg. The sensitivity of clustering method compared with angiography is between 82.75 % and 90.90 %, specificity between 80.66 % and 88.88 %. Using neural networks sensitivity is in range of 79.06 % and 96.87 %, specificity is in range of 73.07 % and 91.30 %.

The thesis addresses the decompilation of high-level object-oriented C++ language from a machine code. The term reverse engineering is defined and existing decompilers are described with emphasis on their ability to reconstruct C++. AVG decompiler project is introduced, to which this thesis contributes. C++ language is analysed, both on a logical level and in the machine code and existing methods of decompilation are described. On this basis a novel method is introduced, capable of decompiling classes, their hierarchy, constructors, destructors and definitions and usages of virtual methods. The method is implemented, tested and evaluated. In the conclusion, several suggestions for future development of this project are presented.