Statistical image reconstruction in computerized tomography

One of the main current goals in computerized tomography (CT) is the optimization of image quality combined with a reduction of the radiation dose given to the patient; this goal is also important for the high resolution micro-CT imaging of small laboratory animals as performed at the VUB. Pursuing ongoing work in the laboratory, this master project involves the development of image reconstruction algorithms that incorporate an accurate model of the physics of the data acquisition and of the statistical properties of the data noise, thereby leading to improved performance especially for data with a low signal-to-noise ratio and/or with a coarse or incomplete sampling. Modern methods (so called "compressed sensing" and "total variation regularization") will be implemented to further improve the robustness to noise. This methodology reduces image reconstruction to a large non-linear optimization problem, which will be solved using iterative techniques. The developed algorithms will be tested using CT data obtained from Monte- Carlo simulations and from measurements on the micro-CT scanner of the laboratory.

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