Statistical Inversion Theory in X-ray Tomography

In this thesis the X-ray tomography is discussed from the Bayesian statistical viewpoint. The unknown parameters are assumed random variables and as opposite to traditional methods the solution is obtained as a large sample of the distribution of all possible solutions. As an introduction to tomography an inversion formula for Radon transform is presented on a plane. The vastly used filtered backprojection algorithm is derived. The traditional regularization methods are presented sufficiently to ground the Bayesian approach. The measurements are foton counts at the detector pixels. Thus the assumption of a Poisson distributed measurement error is justified. Often the error is assumed Gaussian, altough the electronic noise caused by the measurement device can change the error structure. The assumption of Gaussian measurement error is discussed. In the thesis the use of different prior distributions in X-ray tomography is discussed. Especially in severely ill-posed problems the use of a suitable prior is the main part of the whole solution process. In the empirical part the presented prior distributions are tested using simulated measurements. The effect of different prior distributions produce are shown in the empirical part of the thesis. The use of prior is shown obligatory in case of severely ill-posed problem.

Gamma spectrometry and gamma and X-ray tomography of nuclear fuel

The purpose of gamma spectrometry and gamma and X-ray tomography of nuclear fuel is to determine both radionuclide concentration and integrity and deformation of nuclear fuel. The aims of this thesis have been to find out the basics of gamma spectrometry and tomography of nuclear fuel, to find out the operational mechanisms of gamma spectrometry and tomography equipment of nuclear fuel, and to identify problems that relate to these measurement techniques. In gamma spectrometry of nuclear fuel the gamma-ray flux emitted from unstable isotopes is measured using high-resolution gamma-ray spectroscopy. The production of unstable isotopes correlates with various physical fuel parameters. In gamma emission tomography the gamma-ray spectrum of irradiated nuclear fuel is recorded for several projections. In X-ray transmission tomography of nuclear fuel a radiation source emits a beam and the intensity, attenuated by the nuclear fuel, is registered by the detectors placed opposite. When gamma emission or X-ray transmission measurements are combined with tomographic image reconstruction methods, it is possible to create sectional images of the interior of nuclear fuel. MODHERATO is a computer code that simulates the operation of radioscopic or tomographic devices and it is used to predict and optimise the performance of imaging systems. Related to the X-ray tomography, MODHERATO simulations have been performed by the author. Gamma spectrometry and gamma and X-ray tomography are promising non-destructive examination methods for understanding fuel behaviour under normal, transient and accident conditions.

From protein structure to function with bioinformatics

It has long been known that amino acids are the building blocks for proteins and govern their folding into specific three-dimensional structures. However, the details of this process are still unknown and represent one of the main problems in structural bioinformatics, which is a highly active research area with the focus on the prediction of three-dimensional structure and its relationship to protein function. The protein structure prediction procedure encompasses several different steps from searches and analyses of sequences and structures, through sequence alignment to the creation of the structural model. Careful evaluation and analysis ultimately results in a hypothetical structure, which can be used to study biological phenomena in, for example, research at the molecular level, biotechnology and especially in drug discovery and development. In this thesis, the structures of five proteins were modeled with templatebased methods, which use proteins with known structures (templates) to model related or structurally similar proteins. The resulting models were an important asset for the interpretation and explanation of biological phenomena, such as amino acids and interaction networks that are essential for the function and/or ligand specificity of the studied proteins. The five proteins represent different case studies with their own challenges like varying template availability, which resulted in a different structure prediction process. This thesis presents the techniques and considerations, which should be taken into account in the modeling procedure to overcome limitations and produce a hypothetical and reliable three-dimensional structure. As each project shows, the reliability is highly dependent on the extensive incorporation of experimental data or known literature and, although experimental verification of in silico results is always desirable to increase the reliability, the presented projects show that also the experimental studies can greatly benefit from structural models. With the help of in silico studies, the experiments can be targeted and precisely designed, thereby saving both money and time. As the programs used in structural bioinformatics are constantly improved and the range of templates increases through structural genomics efforts, the mutual benefits between in silico and experimental studies become even more prominent. Hence, reliable models for protein three-dimensional structures achieved through careful planning and thoughtful executions are, and will continue to be, valuable and indispensable sources for structural information to be combined with functional data.