A GATE Monte Carlo model for a newly developed small animal PET scanner: the IRI-microPET

S.Z. Islami Rad 1 , R. Gholipour Peyvandi 2 , and M.K. Sadeghi 2
  • 1 Department of Physics, Faculty of Science, University of Qom, Qom
  • 2 Faculty of Physics, Shahrood University of Technology, Shahrood

Abstract

Monte Carlo simulation is widely used in emission tomography, in order to assess image reconstruction algorithms and correction techniques, for system optimization, and study the parameters affecting the system performance. In the current study, the performance of the IRI-microPET system was simulated using the GATE Monte Carlo code and a number of performance parameters, including spatial resolution, scatter fraction, sensitivity, RMS contrast, and signal-to-noise ratio, evaluated and compared to the corresponding measured values. The results showed an excellent agreement between simulated and measured data: The experimental and simulated spatial resolutions (radial) for 18F in the center of the AFOV were 1.81 mm and 1.65 mm, respectively. The difference between the experimental and simulated sensitivities of the system was <7%. Simulated and experimental scatter fractions differed less than 9% for the mouse phantom in different timing windows. The validation study of the image quality indicated a good agreement in RMS contrast and signal-to-noise ratio. Also, system performance was compared with the two available commercial scanners which were simulated using GATE code. In conclusion, the assessment of the Monte Carlo modeling of the IRI-microPET system reveals that the GATE code is a flexible and accurate tool for describing the response of an animal PET system.

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  • [1] Merheb C, Petegnief Y, Talbot N. Full modeling of the MOSAIC animal PET system based on the GATE Monte Carlo simulation code. Phys Med Biol. 2007;52(3):563-576.

  • [2] Merheb C, Nicol S, Petegnief Y et al. Assessment of the Mosaic animal PET system response using list-mode data for validation of Gate Mote Carlo modeling. Nucl Inst Meth A. 2006;569(2):220-224.

  • [3] Rannou FR, Kohli V, Prout DL, et al. Investigation of OPET performance using GATE, a Geant4-based simulation software. IEEE Trans Nucl Sci. 2004;51(5):2713-2717.

  • [4] Schmidtlein CR, Kirov AS, Nehmeh SA, et al. Validation of GATE Monte Carlo simulation of the GE Advance/Discovery LS PET scanners. Med Phys. 2006;33(1):198-208.

  • [5] Lamare F, Turzo A, Bizais Y et al. Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using Gate. Phys Med Biol. 2006;51(4):943-962.

  • [6] Kim JS, Lee JS, Park MJ, et al. Comparative Evaluation of Three MicroPET Series Systems Using Mont Carlo Simulation: Sensitivity and Scatter Fraction. IEEE Nucl Sci Sym Conf. 2007;4534-4535.

  • [7] Islami rad SZ, Gholipour Peyvandi R, Askari lehdarboni M, Ghafari AA. Design and performance evaluation of a high resolution IRI-microPET preclinical scanner. Nucl Instr and Meth A. 2015;781:6-13.

  • [8] Islami rad SZ, Shamsaei Zafarghandi M, Gholipour Peyvandi R et al. Study of the Slow-Fast preamplifier input parameters effects on output image for LYSO scintillator with PS-PMT based animal PET. Instr & Exp Tech. 2014;57(4):488-493.

  • [9] GEANT4 Collaboration, GEANT4: A simulation toolkit, Jun (2009).

  • [10] Canadas M, Embid M, Lage E, et al. NEMA NU 4-2008 performance measurements of two commercial small animal PET scanners: Clear PET and rPET-1. IEEE Trans Nucl Sci. 2011;58(1):58-65.

  • [11] Yang Y, Tai Y, Siegel C, et al. Optimization and performance evaluation of the microPET II scanner for in vivo small-animal imaging. Phys Med Biol. 2004;49(12):2527-2545.

  • [12] Motta A, Damiani C, Del Guerra A, et al. Use of a fast EM algorithm for 3D image reconstruction with the YAP-PET tomograph. Comput Med Imaging Graph. 2002;26(5):293-302.

  • [13] Constantinescu CC, Mukherjee J. Performance evaluation of an Inveon PET preclinical scanner. Phys Med Biol. 2009;54(9):2885-2899.

  • [14] Fahey FH. Data Acquisition in PET Imaging. J Nucl Med Technol. 2002;30(2):39-49.

  • [15] Jiang M, Wang G. Convergence of the simultaneous algebraic reconstruction technique (SART). IEEE Trans Imag Process. 2003;12(8): 957-961.

  • [16] Herman GT. Fundamentals of Computerized Tomography: Image Reconstruction from Projections. Springer (2009).

  • [17] Gholipour Peyvandi R, Islami rad SZ, Heshmati R, et al. Influence of projection steps on image quality using single source–single detector gamma ray tomograph. Instr and Exp Tech. 2011;54(4):542-547.

  • [18] Lodge MA, Rahmim A, Wahl RL. Simultaneous measurement of noise and spatial resolution in PET phantom images. Phys Med Biol. 2010;55(4):1069-1081.

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