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Article type: Research Article
Authors: Hu, Jingjinga; * | Zhao, Xingb
Affiliations: [a] School of Software, Beijing Institute of Technology, Beijing, China | [b] The CT laboratory, School of Mathematical Sciences, Capital Normal University, Beijing, China
Correspondence: [*] Corresponding author: Jingjing Hu, School of Software, Beijing Institute of Technology, Beijing, China. Tel.: + 86 10 68918271; E-mail: hujingjing@bit.edu.cn.
Abstract: X-ray dual spectral CT (DSCT) scans the measured object with two different x-ray spectra, and the acquired rawdata can be used to perform the material decomposition of the object. Direct calibration methods allow a faster material decomposition for DSCT and can be separated in two groups: image-based and rawdata-based. The image-based method is an approximative method, and beam hardening artifacts remain in the resulting material-selective images. The rawdata-based method generally obtains better image quality than the image-based method, but this method requires geometrically consistent rawdata. However, today’s clinical dual energy CT scanners usually measure different rays for different energy spectra and acquire geometrically inconsistent rawdata sets, and thus cannot meet the requirement. This paper proposes a practical material decomposition method to perform rawdata-based material decomposition in the case of inconsistent measurement. This method first yields the desired consistent rawdata sets from the measured inconsistent rawdata sets, and then employs rawdata-based technique to perform material decomposition and reconstruct material-selective images. The proposed method was evaluated by use of simulated FORBILD thorax phantom rawdata and dental CT rawdata, and simulation results indicate that this method can produce highly quantitative DSCT images in the case of inconsistent DSCT measurements.
Keywords: Dual energy computed tomography, basis material decomposition, image quality
DOI: 10.3233/XST-160544
Journal: Journal of X-Ray Science and Technology, vol. 24, no. 3, pp. 407-425, 2016
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