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A Parameter Division Based Method for the Geometrical Calibration of X-Ray Industrial Cone-Beam CT

Authors :
Kai Xiao
Yu Han
Xiaoqi Xi
Bin Yan
Haibing Bu
Lei Li
Source :
IEEE Access, Vol 6, Pp 48970-48977 (2018)
Publication Year :
2018
Publisher :
IEEE, 2018.

Abstract

For X-ray cone-beam computed tomography (CBCT), non-calibrated system geometry causes indistinct and blurring artifacts in reconstructed images, which impacts on the image quality badly. This paper presents a practical industrial CBCT geometric calibration method, which aims to determine the geometric parameter without extra phantom and benefit to efficiency for calibration. In our method, the geometric parameter is divided into system parameter and imaging parameter to be calibrated, respectively. First, the system parameter was determined precisely at the first beginning via average for multiple measurements. Next, the imaging parameter was determined via a designed algorithm in this paper based on the invariance of rotation axis. Furthermore, the proposed method only utilizes the projection data without reconstruction iteration to calibrate geometric parameter, which benefits to efficiency and practical industrial application. 3-D simulation data have been through test with and without noise, and the results show robust and accuracy on the proposed algorithm. In addition, it is also validated with various real data include printed circuit board and industrial components acquired at actual industrial CBCT. The results of reconstructed images demonstrate that the proposed method can achieve comparable image quality in the reconstruction as some phantom-based methods. Furthermore, the advantages and obstacle of the proposed method are analyzed and discussed in this paper.

Details

Language :
English
ISSN :
21693536
Volume :
6
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.575f77a8a52c4207b13805c1a738f0ab
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2018.2865124