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Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging

Author(s): Jun Ma

Journal: Algorithms
ISSN 1999-4893

Volume: 6;
Issue: 1;
Start page: 136;
Date: 2013;
Original page

Keywords: tomographic imaging | penalized likelihood | algorithms | constrained optimization

Image reconstruction is a key component in many medical imaging modalities. The problem of image reconstruction can be viewed as a special inverse problem where the unknown image pixel intensities are estimated from the observed measurements. Since the measurements are usually noise contaminated, statistical reconstruction methods are preferred. In this paper we review some non-negatively constrained simultaneous iterative algorithms for maximum penalized likelihood reconstructions, where all measurements are used to estimate all pixel intensities in each iteration.
RPA Switzerland

RPA Switzerland

Robotic process automation


Tango Jona
Tangokurs Rapperswil-Jona