Paper
19 October 2016 Beam hardening correction for interior tomography based on exponential formed model and radon inversion transform
Siyu Chen, Hanming Zhang, Lei Li, Xiaoqi Xi, Yu Han, Bin Yan
Author Affiliations +
Proceedings Volume 10155, Optical Measurement Technology and Instrumentation; 101553D (2016) https://doi.org/10.1117/12.2247372
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
X-ray computed tomography (CT) has been extensively applied in industrial non-destructive testing (NDT). However, in practical applications, the X-ray beam polychromaticity often results in beam hardening problems for image reconstruction. The beam hardening artifacts, which manifested as cupping, streaks and flares, not only debase the image quality, but also disturb the subsequent analyses. Unfortunately, conventional CT scanning requires that the scanned object is completely covered by the field of view (FOV), the state-of-art beam hardening correction methods only consider the ideal scanning configuration, and often suffer problems for interior tomography due to the projection truncation. Aiming at this problem, this paper proposed a beam hardening correction method based on radon inversion transform for interior tomography. Experimental results show that, compared to the conventional correction algorithms, the proposed approach has achieved excellent performance in both beam hardening artifacts reduction and truncation artifacts suppression. Therefore, the presented method has vitally theoretic and practicable meaning in artifacts correction of industrial CT.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Siyu Chen, Hanming Zhang, Lei Li, Xiaoqi Xi, Yu Han, and Bin Yan "Beam hardening correction for interior tomography based on exponential formed model and radon inversion transform", Proc. SPIE 10155, Optical Measurement Technology and Instrumentation, 101553D (19 October 2016); https://doi.org/10.1117/12.2247372
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KEYWORDS
Radon

Radon transform

Tomography

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