Scatter kernel parameters optimization for digital radiography

Lin Li; Zongqing Lu; Qingmin Liao

doi:10.1117/12.2539635

14 August 2019 Scatter kernel parameters optimization for digital radiography

Lin Li, Zongqing Lu, Qingmin Liao

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111793C (2019) https://doi.org/10.1117/12.2539635
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

Citation Download Citation

Lin Li, Zongqing Lu, and Qingmin Liao "Scatter kernel parameters optimization for digital radiography", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793C (14 August 2019); https://doi.org/10.1117/12.2539635

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