Neural network-based calibration of positron emission tomograph detector modules

In this paper we describe a neural network-based method aimed at automatically calibrating the detector module contained in a scanner for a highresolution positron emission tomography (PET) system for small animals. The detector module is composed of crystal elements, arranged in a regular matrix and sensitive to gamma rays emitted by a radioactive source. The crystal matrix is optically coupled to a position-sensitive photo-multiplier tube, which reconstructs the original image. Calibration, required to cope with spatial distortions introduced by the optical system, consists of a segmentation process of the image produced after the photo-multiplier tube into a fixed number of areas. The purpose of this segmentation is to map each pixel of the perceived image onto the pertinent crystal, which was actually struck by the gamma ray emitted by the radioactive source.