Work

Statistical Interior Tomography

Year: 2011

Type: article

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.

Source: IEEE Transactions on Medical Imaging

DOI: 10.1109/tmi.2011.2106161

Authors: Qiong Xu, Xuanqin Mou, Ge Wang, Jered Sieren, Eric A. Hoffman, Hengyong Yu