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Proceedings Paper

A TV-constrained decomposition method for spectral CT
Author(s): Xiaoyue Guo; Li Zhang; Yuxiang Xing
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Paper Abstract

Spectral CT is attracting more and more attention in medicine, industrial nondestructive testing and security inspection field. Material decomposition is an important issue to a spectral CT to discriminate materials. Because of the spectrum overlap of energy channels, as well as the correlation of basis functions, it is well acknowledged that decomposition step in spectral CT imaging causes noise amplification and artifacts in component coefficient images. In this work, we propose materials decomposition via an optimization method to improve the quality of decomposed coefficient images. On the basis of general optimization problem, total variance minimization is constrained on coefficient images in our overall objective function with adjustable weights. We solve this constrained optimization problem under the framework of ADMM. Validation on both a numerical dental phantom in simulation and a real phantom of pig leg on a practical CT system using dual-energy imaging is executed. Both numerical and physical experiments give visually obvious better reconstructions than a general direct inverse method. SNR and SSIM are adopted to quantitatively evaluate the image quality of decomposed component coefficients. All results demonstrate that the TV-constrained decomposition method performs well in reducing noise without losing spatial resolution so that improving the image quality. The method can be easily incorporated into different types of spectral imaging modalities, as well as for cases with energy channels more than two.

Paper Details

Date Published: 9 March 2017
PDF: 8 pages
Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101323F (9 March 2017); doi: 10.1117/12.2254593
Show Author Affiliations
Xiaoyue Guo, Tsinghua Univ. (China)
Li Zhang, Tsinghua Univ. (China)
Yuxiang Xing, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 10132:
Medical Imaging 2017: Physics of Medical Imaging
Thomas G. Flohr; Joseph Y. Lo; Taly Gilat Schmidt, Editor(s)

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