title: Statistical interior tomography.
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.
authors.affiliation: Institute of Image Processing and Pattern Recognition, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China.
authors.forename: Qiong
authors.initials: Q
authors.lastname: Xu
authors.forename: Xuanqin
authors.initials: X
authors.lastname: Mou
authors.forename: Ge
authors.initials: G
authors.lastname: Wang
authors.forename: Jered
authors.initials: J
authors.lastname: Sieren
authors.forename: Eric A
authors.initials: EA
authors.lastname: Hoffman
authors.forename: Hengyong
authors.initials: H
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D019047:Phantoms, Imaging; D016012:Poisson Distribution; D013902:Radiography, Thoracic; D015203:Reproducibility of Results; D012756:Sheep; D012815:Signal Processing, Computer-Assisted; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*