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Author Oliver, J.F.; Fuster-Garcia, E.; Cabello, J.; Tortajada, S.; Rafecas, M.
Title (down) Application of Artificial Neural Network for Reducing Random Coincidences in PET Type Journal Article
Year 2013 Publication IEEE Transactions on Nuclear Science Abbreviated Journal IEEE Trans. Nucl. Sci.
Volume 60 Issue 5 Pages 3399-3409
Keywords
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%.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0018-9499 ISBN Medium
Area Expedition Conference
Notes WOS:000325827200027 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 1611
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Author Solevi, P. et al; Oliver, J.F.; Gillam, J.E.; Rafecas, M.
Title (down) A Monte-Carlo based model of the AX-PET demonstrator and its experimental validation Type Journal Article
Year 2013 Publication Physics in Medicine and Biology Abbreviated Journal Phys. Med. Biol.
Volume 58 Issue 16 Pages 5495-5510
Keywords
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.
Address [Solevi, P.; Oliver, J. F.; Gillam, J. E.; Rafecas, M.] Univ Valencia, CSIC, IFIC, E-46071 Valencia, Spain, Email: paola.solevi@ific.uv.es
Corporate Author Thesis
Publisher Iop Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-9155 ISBN Medium
Area Expedition Conference
Notes WOS:000322775300012 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 1544
Permanent link to this record