Position-Sensitive Silicon Photomultiplier Array with Enhanced Position Reconstruction by means of a Deep Neural Network

TL;DR

This paper demonstrates that using deep neural networks for position reconstruction in silicon photomultiplier arrays significantly improves resolution, linearity, and the number of resolved pixels, enhancing medical imaging capabilities.

Contribution

The study introduces a DNN-based method for position reconstruction in LG-SiPM arrays, outperforming traditional formulas in resolution and linearity.

Findings

DNN improves position resolution and linearity.

Boosts resolved pixel count by up to 12.1 times.

Enhances imaging performance in medical applications.

Abstract

Single-photon sensitive detectors like Silicon Photomultipliers are widely used in many medical imaging applications. By using detectors with position resolutions, it is possible to build compact photodetector readouts with reduced number of channels, but still preserving position resolution and gamma-rays imaging capabilities. In this work, we present the advantage of using a Deep Neural Networks (DNNs) light position reconstruction applied to a 2x2 array of linearly-graded SiPMs (LG-SiPMs), to minimize the distortions on the reconstructed event maps. Our approach significantly enhances both the resolution and linearity of position detection compared to the nominal reconstruction formula based on the device architecture. Remarkably, the DNN-based reconstruction boosts the number of resolved areas (pixels) by a factor of 5.7 to 12.1 (depending the training splitting used) allowing for a higher level of precision and performance in light detection.