A 2D Positioning Application in PET Using ANNs

Positron Emission Tomography (PET) is a radiotracer imaging technique based on the administration (typically by injection) of compounds labelled with positron emitting radionuclides to a patient under study. When the radio-isotope decays, it emits a positron, which travels a short distance before annihilating with an electron. This annihilation produces two high-energy (511 keV) gamma photons propagating in nearly opposite directions, along an imaginary line called Line of Response (LOR). In PET imaging, the photons emitted by the decaying isotope are detected with gamma cameras. These cameras consist of a lead collimator to ensure that all detected photons are propagated along parallel paths, a crystal scintillator to convert high-energy photons to visible light, photo-multiplier tubes (PMT) to transform light signals into electric signals, and associated electronics to determine the position of each incident photon from the light distribution in the crystal (Ollinger & Fessler, 1997). We have researched on how Artificial Neural Networks (henceforth ANNs or NNs) could be used for bias-corrected position estimation. Small-scale ANNs like the ones considered in this work can be easily implemented in hardware, due to their highly parallelizable structure. Therefore, we have tried to take advantage of the capabilities of ANNs for modelling the real detector response. BACKGROUND