Gamma-Ray Position-of-Interaction Estimation in a Thick Monolithic LaBr3 Detector Using Artificial Neural Networks

Monolithic gamma-ray detectors can be used in single photon emission computed tomography systems for monitoring the delivered dose during boron neutron capture therapy treatments. Gamma-ray hit localization in thick monolithic scintillator crystals is a challenging task due to internal reflections and Compton scattering. Existing methods like the center of gravity (CoG) are susceptible to reconstruction uncertainties at the crystal edges, while approaches, including nonlinear analytical and statistical models, such as the maximum-likelihood, require significant computational resources. Artificial neural networks (ANNs) offer significant improvements in terms of accuracy and computational speed. In this study, we develop a supervised ANN regression algorithm for real-time position reconstruction in a thick square lanthanum bromide crystal [LaBr3(Ce+Sr) ] with 5cm× 5cm ×2×cm dimensions, coupled with an 8×8 matrix of silicon photomultipliers. The implemented neural network was trained and tested using calibration data acquired irradiating the crystal with a collimated 137Cs source (pencil-beam irradiation). The detector in combination with the ANN model achieves a positioning accuracy for single-gamma-ray events of approximately 2.6 mm in the central region, evaluated as the full width at half maximum (FWHM) of the prediction error distribution, slightly worsening toward the edges. The imaging capabilities of the detector in combination with a channel-edge pinhole collimator were then evaluated by acquiring images of a movable uncollimated 137Cs point source. The source was shifted in nine different positions at 3 mm distance from each other and the resolution of the system was evaluated fitting the images with a Gaussian curve. An image spatial resolution of around 8 mm FWHM was obtained, dominated as expected by the collimator geometry, with an accuracy of 0.7 mm in estimating the position of the point source.