A study on the positioning of fine scintillation pixels in a positron emission tomography detector through deep learning of simulation data

Abstract

In order to specify the location of the scintillation pixel that interacted with gamma rays in the positron emission tomography (PET) detector, conventionally, after acquiring a flood image, the location of interaction between the scintillation pixel and gamma ray could be specified through a pixel-segmentation process. In this study, the experimentally acquired signal was specified as the location of the scintillation pixel directly, without any conversion process, through the simulation data and the deep learning algorithm. To evaluate the accuracy of the specification of the scintillation pixel location through deep learning, a comparative analysis with experimental data through pixel segmentation was performed. In the same way as in the experiment, a detector was configured on the simulation, a model was built using the acquired data through deep learning, and the location was specified by applying the experimental data to the built model. Accuracy was calculated through comparative analysis between the specified location and the location obtained through the segmentation process. As a result, it showed excellent accuracy of about 85 %. When this method is applied to a PET detector, the position of the scintillation pixel of the detector can be specified simply and conveniently, without additional work.© 2017 Elsevier Inc. All rights reserved.