Data Support of Generalization Ability of a CNN γ-ray Localization Model for Radiation Imaging

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.