Abstract
The main purpose of this paper is to present the properties of our novel statistical model-based iterative approach to the image reconstruction from projections problem regarding its condition number. The reconstruction algorithm based on this concept uses a maximum likelihood estimation with an objective adjusted to the probability distribution of measured signals obtained using x-ray computed tomography. We compare this with some selected methods of regularizing the problem. The concept presented here is fundamental for 3D statistical tailored reconstruction methods designed for x-ray computed tomography.
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Cierniak, R., Lorent, A., Pluta, P., Shah, N. (2016). Regularization Methods for the Analytical Statistical Reconstruction Problem in Medical Computed Tomography. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_13
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DOI: https://doi.org/10.1007/978-3-319-39384-1_13
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