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Comparison of an adaptive neuro-fuzzy inference system and an artificial neural network in the cross-talk correction of simultaneous 99mTc / 201Tl SPECT imaging using a GATE Monte-Carlo simulation

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Abstract

The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) to estimate the cross-talk contamination of 99m Tc / 201 Tl image acquisition in the 201 Tl energy window (77 ± 15% keV). GATE (Geant4 Application in Emission and Tomography) is employed due to its ability to simulate multiple radioactive sources concurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. In the real and the simulation studies, data acquisition is carried out using eight energy windows. The ANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training data set. Three indications are evaluated and compared. The ANFIS method yields better outcomes for two indications (Spearman’s rank correlation coefficient and contrast) and the two phantom results in each category. The maximum image biasing, which is the third indication, is found to be 6% more than that for the ANN.

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Authors and Affiliations

  1. Department of Energy Engineering and Physics, Amir-Kabir University of Technology, Tehran, Iran

    Saeed Heidary & Saeed Setayeshi

  2. Nuclear Science and Technology Research Institute, Tehran, Iran

    Mohammad Ghannadi-Maragheh

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  1. Saeed Heidary
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  2. Saeed Setayeshi
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  3. Mohammad Ghannadi-Maragheh
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Correspondence to Saeed Setayeshi.

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Heidary, S., Setayeshi, S. & Ghannadi-Maragheh, M. Comparison of an adaptive neuro-fuzzy inference system and an artificial neural network in the cross-talk correction of simultaneous 99mTc / 201Tl SPECT imaging using a GATE Monte-Carlo simulation. Journal of the Korean Physical Society 65, 778–785 (2014). https://doi.org/10.3938/jkps.65.778

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  • DOI: https://doi.org/10.3938/jkps.65.778

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