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Statistical Interior Tomography
Journal article   Peer reviewed

Statistical Interior Tomography

Qiong Xu, Xuanqin Mou, Ge Wang, Jered Sieren, Eric A Hoffman and Hengyong Yu
IEEE transactions on medical imaging, Vol.30(5), pp.1116-1128
05/2011
DOI: 10.1109/TMI.2011.2106161
PMCID: PMC3246757
PMID: 21233044

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Abstract

This paper presents a statistical interior tomography (SIT) approach making use of compressed sensing (CS) theory. With the projection data modeled by the Poisson distribution, an objective function with a total variation (TV) regularization term is formulated in the maximization of a posteriori (MAP) framework to solve the interior problem. An alternating minimization method is used to optimize the objective function with an initial image from the direct inversion of the truncated Hilbert transform. The proposed SIT approach is extensively evaluated with both numerical and real datasets. The results demonstrate that SIT is robust with respect to data noise and down-sampling, and has better resolution and less bias than its deterministic counterpart in the case of low count data.
Computed Tomography Algorithm design and analysis truncated Hilbert transform computed tomography (CT) statistical iterative reconstruction Compressed sensing (CS) Transforms Minimization interior tomography Image reconstruction

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