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A sequential regularization based image reconstruction method for limited-angle spectral CT
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DOI：10.1088/1361-6560/ab9771.png)
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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.
Keyword:
spectral CT
limited-angle imaging
optimization-based reconstruction
sequential regularization
期刊
IF:
3.4
论文数:
1.4W
被引数:
3.1W
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