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Oliver, J. F., Fuster-Garcia, E., Cabello, J., Tortajada, S., & Rafecas, M. (2013). Application of Artificial Neural Network for Reducing Random Coincidences in PET. IEEE Trans. Nucl. Sci., 60(5), 3399–3409.
Abstract: Positron Emission Tomography (PET) is based on the detection in coincidence of the two photons created in a positron annihilation. In conventional PET, this coincidence identification is usually carried out through a coincidence electronic unit. An accidental coincidence occurs when two photons arising from different annihilations are classified as a coincidence. Accidental coincidences are one of the main sources of image degradation in PET. Some novel systems allow coincidences to be selected post-acquisition in software, or in real time through a digital coincidence engine in an FPGA. These approaches provide the user with extra flexibility in the sorting process and allow the application of alternative coincidence sorting procedures. In this work a novel sorting procedure based on Artificial Neural Network (ANN) techniques has been developed. It has been compared to a conventional coincidence sorting algorithm based on a time coincidence window. The data have been obtained from Monte-Carlo simulations. A small animal PET scanner has been implemented to this end. The efficiency (the ratio of correct identifications) can be selected for both methods. In one case by changing the actual value of the coincidence window used, and in the other by changing a threshold at the output of the neural network. At matched efficiencies, the ANN-based method always produces a sorted output with a smaller random fraction. In addition, two differential trends are found: the conventional method presents a maximum achievable efficiency, while the ANN-based method is able to increase the efficiency up to unity, the ideal value, at the cost of increasing the random fraction. Images reconstructed using ANN sorted data (no compensation for randoms) present better contrast, and those image features which are more affected by randoms are enhanced. For the image quality phantom used in the paper, the ANN method decreases the spill-over ratio by a factor of 18%.
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Solevi, P. et al, Oliver, J. F., Gillam, J. E., & Rafecas, M. (2013). A Monte-Carlo based model of the AX-PET demonstrator and its experimental validation. Phys. Med. Biol., 58(16), 5495–5510.
Abstract: AX-PET is a novel PET detector based on axially oriented crystals and orthogonal wavelength shifter (WLS) strips, both individually read out by silicon photo-multipliers. Its design decouples sensitivity and spatial resolution, by reducing the parallax error due to the layered arrangement of the crystals. Additionally the granularity of AX-PET enhances the capability to track photons within the detector yielding a large fraction of inter-crystal scatter events. These events, if properly processed, can be included in the reconstruction stage further increasing the sensitivity. Its unique features require dedicated Monte-Carlo simulations, enabling the development of the device, interpreting data and allowing the development of reconstruction codes. At the same time the non-conventional design of AX-PET poses several challenges to the simulation and modeling tasks, mostly related to the light transport and distribution within the crystals and WLS strips, as well as the electronics readout. In this work we present a hybrid simulation tool based on an analytical model and a Monte-Carlo based description of the AX-PET demonstrator. It was extensively validated against experimental data, providing excellent agreement.
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