Mr. Kai Xiao Profile

Kai Xiao

at National Digital Switching System Engineering

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Author

Publications (2)

Proceedings Article | 10 May 2019 Paper

Metal artifact reduction based on fully convolutional networks in CT image domain

Linlin Zhu, Yu Han, Lei Li, Yifu Xu, Xiaoqi Xi, Bin Yan, Kai Xiao

Proceedings Volume 11068, 110681U (2019) https://doi.org/10.1117/12.2524635

KEYWORDS: Metals, X-ray computed tomography, Computed tomography, Image processing, Convolution, Deconvolution, X-rays

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Proceedings Article | 24 October 2017 Paper

An optimized acquisition approach exploiting geometrical calibration in x-ray cone-beam computed tomography

Kai Xiao, Yu Han, Xiaoqi Xi, Haibing Bu, Lei Li, Bin Yan, Yanmin Sun, Jingyu Wang, Jianbang Liu

Proceedings Volume 10462, 104624Q (2017) https://doi.org/10.1117/12.2285578

KEYWORDS: Calibration, X-ray computed tomography, X-ray imaging, Image restoration

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X-ray cone-beam computed tomography, featuring high precision and fast-imaging speed, has been widely used in industrial non-destructive testing applications for the three dimensional visualization of internal structures. Due to mechanical imperfections, geometric calibrations are imperative to high quality image reconstruction. Currently, the twoball phantom-based calibration procedures exploiting the projection trajectories of the phantoms are the most commonly used approach for the estimation of the geometrical parameters and the calibration of CT system. However, an additional scan needs to be performed, even after each object acquisition when lack of system reproducibility, leading to multiplied calibration times. The emphasis of this paper is to optimize the process of acquisition in cone-beam CT imaging with minimal time, based on the understanding of the determination of the ball position in typical phantom-based geometric calibration algorithms. An applicable condition of the calibration algorithm for simultaneously scanning objects and calibration phantoms is proposed and demonstrated, which is that the minimum projection value of the scanned object needs to be at least 100 counts higher than those of the calibration phantom, with consideration of the system noise. The CT experiments are based on a laboratory industrial cone-beam CT system with a micro-focus x-ray tube (Thales Hawkeye 130) and a flat panel detector (Thales Pixium RF4343). Objects imaged are chosen with a wide projection value range, from low-Z watermelon seeds and high-Z materials, including a standard Micro CT Bar Pattern Phantom (QRM) for image quality assessment. In these experiments, objects, as well as two-ball phantoms, both placed in the field of view without overlapping in the vertical direction, are projected over 360 degrees, instead of scanning the calibration phantoms separately. Hence, the true geometrical relationship is resolved utilizing the two-ball algorithm. Both simulation and experimental results confirm that the calculated geometrical parameters will not be affected by the objects as long as their projection value difference meeting the requirements above. And the reconstruction image quality is almost the same with those by an independent calibration. Compared to the traditional application of the phantombased geometrical calibration method, the novel approach presented in this paper has obvious advantages from an imaging perspective, saving acquisition time and eliminating the undesired influence from the operation staff for the same cost.

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