Optimization of grid-less scattering compensation in X-ray imaging: Simulation study

Danyk, A. Y.; Radchenko, S. P.; Sudakov, Oleksandr

doi:10.1109/elnano.2017.7939770

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…The testing simulation was made for several exposure times. Scattering compensation was performed for scattering kernels libraries shrunk to the different number of scattering kernels by clustering analysis [6,7]. Scattering compensation quality was estimated using error definition (9).…”

Section: Resultsmentioning

confidence: 99%

“…where  and  are constants dependent on absorption and scattering coefficients, rspherical coordinates system radius. Equation (6) gives scattering kernel for uniform and isotropic medium, monochromatic radiation, and elastic scattering. Realistic cases will introduce perturbations into (6).…”

Section: Scattering Kernels Parametrizationmentioning

confidence: 99%

“…Equation (6) gives scattering kernel for uniform and isotropic medium, monochromatic radiation, and elastic scattering. Realistic cases will introduce perturbations into (6).…”

Section: Scattering Kernels Parametrizationmentioning

confidence: 99%

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Physical bases for determination of scattering kernels from incomplete data in grid-less X-ray imaging

Danyk¹,

Sudakov²

2021

Nucl. Phys. At. Energy

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A mathematical model for the determination of X-ray scattering kernels’ shapes based on incomplete simulation or measurement data was introduced and tested using a mathematical phantom. The model is originally intended for low-dose X-ray imaging without anti-scatter grids. The proposed model fits different kinds of symmetrical and asymmetrical scattering kernels in different tissues well enough for practical applications. Kernels asymmetry is mostly caused by irradiation of the object near the boundaries of different tissues. The model describes a variety of asymmetrical kernels by proposed “sectoral” members. Application of the proposed model in scattering compensation procedure reduces resulting error up to 50 % for “wide” scattering kernels.

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Section: Resultsmentioning

confidence: 99%

Section: Scattering Kernels Parametrizationmentioning

confidence: 99%