Conference Paper

Penalized-likelihood sinogram restoration for CT artifact correction

Dept. of Radiol., Chicago Univ., IL, USA
DOI: 10.1109/NSSMIC.2004.1466396 Conference: Nuclear Science Symposium Conference Record, 2004 IEEE, Volume: 5
Source: IEEE Xplore


In CT sinogram preprocessing, the best possible estimate of the line integrals needed for image reconstruction from the set of noisy, degraded detector measurements is reported. A general imaging model relating the degraded measurements to the ideal sinogram and the estimation of the ideal line integrals by iteratively maximizing an appropriate penalized statistical likelihood function are discussed. Image reconstruction is then performed by the use of existing non-iterative approaches.

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Available from: Patrick Jean La Riviere, Mar 10, 2015
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    • "Our general strategy thus far [2] [3] [4] has been to maximize a penalized-likelihood objective function "
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    ABSTRACT: We have compared the performance of two different penalty choices for a penalized-likelihood sinogram-restoration strategy we have been developing. One is a quadratic penalty we have employed previously and the other is a new median-based penalty. We compared the approaches to a noniterative adaptive filter that loosely but not explicitly models data statistics. We found that the two approaches produced similar resolution-variance tradeoffs to each other and that they outperformed the adaptive filter in the low-dose regime, which suggests that the particular choice of penalty in our approach may be less important than the fact that we are explicitly modeling data statistics at all. Since the quadratic penalty allows for derivation of an algorithm that is guaranteed to monotonically increase the penalized-likelihood objective function, we find it to be preferable to the median-based penalty.
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