Investigation of an in situ position calibration method for continuous crystal-based PET detectors

The absence of very small crystal pixels in monolithic scintillation detectors has a number of potential advantages such as higher sensitivity, better energy resolution and continuous coordinates. In such detectors, the photon incidence position on the detector surface is derived from the measured scintillation light distribution using artificial neural networks (NNs). To this end, each detector module has to be position-calibrated by training the NNs. An automated procedure to simultaneously obtain the calibration data to train NNs for all detector modules in a fully assembled PET system has been developed and evaluated on a simulator set-up.