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

Algorithms

关键词: tomographic imaging;    penalized likelihood;    algorithms;    constrained optimization;   

DOI  :  10.3390/a6010136

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the author; licensee MDPI, Basel, Switzerland.