Abstract：
:Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.
journal_name
Med Physjournal_title
Medical physicsauthors
Zhang H,Wang J,Zeng D,Tao X,Ma Jdoi
10.1002/mp.13123subject
Has Abstractpub_date
2018-10-01 00:00:00pages
e886-e907issue
10eissn
0094-2405issn
2473-4209journal_volume
45pub_type
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