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Article type: Research Article
Authors: He, Peng; | Yu, Hengyong; ; | Bennett, James | Ronaldson, Paul | Zainon, Rafidah | Butler, Anthony; | Butler, Phil; | Wei, Biao | Wang, Ge; ;
Affiliations: The Key Lab of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing, China | Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA, USA | Department of Radiology, Division of Radiologic Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, USA | Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Wake Forest University Health Sciences, Winston-Salem, NC, USA | Department of Radiology, University of Otago, Christchurch, New Zealand | Department of Physics and Astronomy, University of Canterbury, Christchurch, New Zealand | European Organization for Nuclear Research, Geneva, Switzerland
Note: [] Corresponding authors: Hengyong Yu, Ge Wang, Biomedical Imaging Division, VT-WFU School of Biomedical Engineering and Sciences, Virginia Tech, Blacksburg, VA 24061, USA. E-mail: hengyong-yu@ieee.org; ge-wang@ieee.org
Abstract: Experiments were performed to evaluate the energy-discriminative performance of a spectral (multi-energy) micro-CT system. The system, designed by MARS (Medipix All Resolution System) Bio-Imaging Ltd. (Christchurch, New Zealand), employs a photon-counting energy-discriminative detector technology developed by CERN (European Organization for Nuclear Research). We used the K-edge attenuation characteristics of some known materials to calibrate the detector's photon energy discrimination. For tomographic analysis, we used the compressed sensing (CS) based ordered-subset simultaneous algebraic reconstruction techniques (OS-SART) to reconstruct sample images, which is effective to reduce noise and suppress artifacts. Unlike conventional CT, the principal component analysis (PCA) method can be applied to extract and quantify additional attenuation information from a spectral CT dataset. Our results show that the spectral CT has a good energy-discriminative performance and provides more attenuation information than the conventional CT.
Keywords: Energy-discriminative performance, spectral CT, K-edge imaging, compressive sensing, iterative reconstruction, principal component analysis
DOI: 10.3233/XST-130382
Journal: Journal of X-Ray Science and Technology, vol. 21, no. 3, pp. 335-345, 2013
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