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Generalization ability of a CNNγ-ray localization model for radiation imaging 被引量:1
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作者 wei Lu hai‑wei zhang +3 位作者 Ming‑Zhe Liu Hao‑Xuan Li Xian‑Guo Tuo Lei Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2023年第12期53-65,共13页
Inγ-ray imaging,localization of theγ-ray interaction in the scintillator is critical.Convolutional neural network(CNN)techniques are highly promising for improvingγ-ray localization.Our study evaluated the generali... Inγ-ray imaging,localization of theγ-ray interaction in the scintillator is critical.Convolutional neural network(CNN)techniques are highly promising for improvingγ-ray localization.Our study evaluated the generalization capabilities of a CNN localization model with respect to theγ-ray energy and thickness of the crystal.The model maintained a high positional linearity(PL)and spatial resolution for ray energies between 59 and 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 and 1.19 mm.In modified uniform redundant array(MURA)imaging systems using a thick crystal,the CNNγ-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 of the reconstructed images increased from 0.95 to 5.63. 展开更多
关键词 γ-Ray imaging γ-Ray localization model Convolutional neural network Spatial resolution
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