Open AccessA sequential regularization based image reconstruction method for limited-angle spectral CT
Author(s) -
Wenjuan Sheng,
Xing Zhao,
Mengfei Li
Publication year - 2020
Publication title -
physics in medicine and biology/physics in medicine and biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.312
H-Index - 191
eISSN - 1361-6560
pISSN - 0031-9155
DOI - 10.1088/1361-6560/ab9771
Subject(s) - regularization (linguistics) , monochromatic color , iterative reconstruction , computer science , computer vision , artificial intelligence , projection (relational algebra) , solver , tomography , mathematics , algorithm , optics , physics , mathematical optimization
In spectral computed tomography (CT), the object is respectively scanned under different x-ray spectra. Multiple projection data can be collectively used for reconstructing basis images and virtual monochromatic images, which have been used in material decomposition, beam-hardening correction, bone removal, and so on. In practice, projection data may be obtained in a limited scanning angular range. Images reconstructed from limited-angle data by conventional spectral CT reconstruction methods will be deteriorated by limited-angle related artifacts and basis image decomposition errors. Motivated by observations of limited-angle spectral CT, we propose a sequential regularization-based limited-angle spectral CT reconstruction model and its numerical solver. Both simulated and real data experiments validate that our method is capable of suppressing artifacts, preserving edges and reducing decomposition errors.