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Scatter kernel parameters optimization for digital radiography
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Paper Abstract

In digital radiography, the interaction between X-ray with object causes scattered radiation that reduces the contrast of image. Scatter kernel superposition (SKS), a computerised scatter correction method, would remove scattering in digital X-ray images. Parameters of scatter kernels in SKS are commonly obtained using Monte Carlo N-Particle Transport Code (MCNP) simulation. However, simulated scatter kernel has bias compared to physical scatter characteristic, related to errors of physical parameters of device and MCNP simulation. Because hyper-parameters in scatter kernel are difficult to optimize, we introduce Bayesian optimization to further optimize the parameters. According to the results of phantom and clinical experiments, our method improves contrast and the peak signal-to-noise ratio of images compared to traditional SKS.

Paper Details

Date Published: 14 August 2019
PDF: 10 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793C (14 August 2019); doi: 10.1117/12.2539635
Show Author Affiliations
Lin Li, Tsinghua Univ. (China)
Zongqing Lu, Tsinghua Univ. (China)
Qingmin Liao, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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