Regression-based gain optimization method to compensate inter-channel variations for monolithic scintillation detectors in gamma-ray imaging system

Abstract

Monolithic scintillation detectors are widely used in gamma-ray imaging systems owing to their numerous advantages. For effective imaging, accurate estimation of interaction information (e.g., energy and position) of gamma rays within the detector is essential. This estimation is typically based on analyzing the sum and ratios of multiple photosensor signals. While the sum of these signals is theoretically proportional to the initial scintillation intensity, intrinsic variations between photosensor channels—caused by differing photosensor gains and geometric asymmetries—can introduce bias in energy estimation. Additionally, photosensor responses can fluctuate under varying operating conditions, leading to significant inaccuracies in energy and position estimation over time. In this study, we propose a method that compensates for inter-channel response variations resulting from both intrinsic and operational factors using only source flood data. Our method derives a gain optimization matrix through regression analysis of flood data and applies it to the photosensor signals for compensation. This approach improved the energy resolution at 662 keV from 11.1 % to 6.8 % by effectively addressing intrinsic variations. Even with significant photosensor gain variations, the method restored the degraded energy resolution and position estimation accuracy. These results show our method successfully compensates for operational variations, ensuring stable imaging performance in real-world scenarios.