2026-10-14T20:05:43Z https://www.scidb.cn/oai

10.57760/sciencedb.j00186.00287 2023-10-15T09:52:16Z

2023-10-15 Data Support of Generalization Ability of a CNN γ-ray Localization Model for Radiation Imaging doi:10.57760/sciencedb.j00186.00287 en In γ-ray imaging, localization of the γ-ray interaction in the scintillator is critical. Convolutional neural network (CNN) techniques are highly promising for improving γ-ray localization. Our study evaluated the generalization capabilities of a CNN localization model with respect to the γ-ray energy and thickness of the crystal. The model maintained a high positional linearity (PL) and spatial resolution (SR) for ray energies between 59–1460 keV. The PL at the incident surface of the detector was 0.99, and the resolution of the central incident point source ranged between 0.52–1.19 mm. In modified uniform redundant array (MURA) imaging systems using a thick crystal, the CNN γ-ray localization model significantly improved the useful field-of-view (UFOV) from 60.32% to 93.44% compared to the classical centroid localization methods. Additionally, the signal-to-noise ratio (SNR) of the reconstructed images increased from 0.95 to 5.63. γ-ray imaging; γ-ray localization model; convolutional neural network LU Wei Zhang Haiwei LIU Ming-zhe Li Hao-xuan TUO Xian-guo WANG Lei PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1007/s41365-023-01323-y Science Data Bank