The correct determination of the position of incident photons is a crucial issue in PET imaging. In this paper we study the use of Neural Networks (NNs) for position estimation of photons impinging on gamma-ray detector modules for PET cameras based on continuous scintillators and Multi-Anode Photomultiplier Tubes (MA-PMTs). We have performed a thorough analysis of the NN architecture and training procedures, using realistic simulated inputs, in order to achieve the best results in terms of spatial resolution and bias correction. The results confirm that NNs can partially model and correct the non-uniform detector response using only the position-weighted signals from a simple 2D Discretized Positioning Circuit (DPC). Linearity degradation for oblique incidence is also investigated. Finally, the NN can be implemented in hardware for parallel real time corrected Line-of-Response (LOR) estimation. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Mateo, F., Aliaga, R. J., Martínez, J. D., Monzó, J. M., & Gadea, R. (2007). Incidence position estimation in a PET detector using a discretized positioning circuit and neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4507 LNCS, pp. 684–691). Springer Verlag. https://doi.org/10.1007/978-3-540-73007-1_82
Mendeley helps you to discover research relevant for your work.