Information content in nuclear medicine imaging

Abstract

The aim of the present study was to investigate the information content of positron emission tomography (PET) images. We used the GATE Monte Carlo package (GEANT4 application for tomographic emission) and reconstructed images, obtained using the software for tomographic image reconstruction (STIR). The case study for the investigation of the PET images information content was the General Electric Discovery-ST (USA) PET scanner. A thin film plane source aluminum (Al) foil, coated with a thin layer of silica and a fluorodeoxy glucose (18F-FDG) bath distribution of (1 MBq) was used in the simulation for the image signal to noise ratio assessment. The influence of the maximum likelihood estimation ordered subsets maximum a posteriori one step late (MLE)-OS-MAP-OSL algorithm, using various subsets (1 to 21) and iterations (1 to 20) was examined. The image information content was assessed in terms of the information capacity (IC). Results showed that the single index information capacity maximized for the range of 8-20 iterations and 3 subsets. In conclusion, our study showed that the image information content of PET scanners can be fully characterized and further improved by investigation of the imaging chain components through Monte Carlo methods. Moreover, new perspectives are created by using the suggested techniques in the context of a global cloud service that could serve as an online quality evaluation metric for the PET scanners and other medical imaging systems. © 2019 The Authors. Published by Elsevier Ltd.