Incidence position estimation in a PET detector using a discretized positioning circuit and neural networks

  • Authors:

  • Fernando Mateo;Ramón José Aliaga;Jorge Daniel Martínez;José María Monzó;Rafael Gadea

  • Affiliations:

  • Departamento de Ingeniería Electrónica, Universidad Politécnica de Valencia, Valencia, Spain;Departamento de Ingeniería Electrónica, Universidad Politécnica de Valencia, Valencia, Spain;Departamento de Ingeniería Electrónica, Universidad Politécnica de Valencia, Valencia, Spain;Departamento de Ingeniería Electrónica, Universidad Politécnica de Valencia, Valencia, Spain;Departamento de Ingeniería Electrónica, Universidad Politécnica de Valencia, Valencia, Spain

  • Venue:
  • IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
  • Year:
  • 2007

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Abstract

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.