DOAJ Open Access 2026

Scattering correction in fast neutron radiography based on Gaussian fitting model of point scattered function

Wangtao Yu Peng Xu Man Zhou Jie Bao Yu Wang

Abstrak

Fast neutron radiography (FNR) offers significant advantages for the nondestructive testing of large-scale and high-density materials. However, the presence of scattered neutrons results in image blurring and distortion, which severely compromises both the imaging quality and the accuracy of quantitative analysis. This study employed the Geant4 Monte Carlo code to simulate the point scattered function (PScF) data. A Gaussian model was subsequently applied to fit the obtained data, with particular emphasis on two key parameters of Maximum and full width at half maximum (FWHM). The effects of sample thickness and detection distance on the PScF parameters were systematically investigated for three typical materials of iron (Fe), tungsten (W) and polyethylene (PE). Through curve-fitting procedures, analytical expressions were derived to characterize the relationships of Maximum and FWHM with respect to sample thickness and detection distance, thereby enabling the rapid calculation of the PScF for any given set of parameters. The developed PScF model was utilized to perform scattering correction on fast neutron images obtained by Geant4 simulations. The results indicate that this method can effectively suppresses the scattered neutron component, leading to a remarkable enhancement in image quality and quantitative analysis accuracy, which validates the effectiveness of the proposed method.

Penulis (5)

W

Wangtao Yu

P

Peng Xu

M

Man Zhou

J

Jie Bao

Y

Yu Wang

Format Sitasi

Yu, W., Xu, P., Zhou, M., Bao, J., Wang, Y. (2026). Scattering correction in fast neutron radiography based on Gaussian fitting model of point scattered function. https://doi.org/10.1016/j.net.2025.103980

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →
Lihat di Sumber doi.org/10.1016/j.net.2025.103980
Informasi Jurnal
Tahun Terbit
2026
Sumber Database
DOAJ
DOI
10.1016/j.net.2025.103980
Akses
Open Access ✓