Design and performance evaluation of a high resolution IRI-microPET preclinical scanner

rad, S. Z. Islami; Peyvandi, R. Gholipour; Lehdarboni, M.A.; Ghafari, A.A.

doi:10.1016/j.nima.2015.01.093

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…The IRI-microPET (Islamic Republic of Iran) was constructed and designed at the NSTRI [7]. The scanner consists of four detectors so that each detector was positioned at a distance of 50 mm from the axis of rotation on a rotating gantry.…”

Section: Iri-micropet: System Descriptionmentioning

confidence: 99%

“…Images were reconstructed with the implementation of the MLEM algorithm on acquired ASCII sinogram data by using an in-house program written in MATLAB (R2015a). Finally, the code was validated via comparison with measured data for NEMA measurements of the IRI-micro PET scanner published by Islami rad et al [7,9].…”

Section: Data Outputmentioning

confidence: 99%

“…The MLEM algorithm was used to reconstruct the acquired sinograms. Then, the spatial resolution was measured as the FWHM of the profiles (Full Width at Half-Maximum) in the radial directions [7,10,11]. These measurements were performed in real condition.…”

Section: Spatial Resolutionmentioning

confidence: 99%

“…In this research, GATE (Geant 4 Application for Tomographic Emission-version 5.0.0) was presented as a Monte Carlo simulation toolkit for simulating and validation an animal positron emission tomography system (IRI-microPET). In order to optimize image quality and overall performance, this system had been built and tested at the Nuclear Science and Technology Research Institute (NSTRI-Gamma scan Laboratory) [7].…”

Section: Introductionmentioning

confidence: 99%

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

rad

Peyvandi

Sadeghi

2019

Polish Journal of Medical Physics and Engineering

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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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Section: Iri-micropet: System Descriptionmentioning

confidence: 99%

Section: Data Outputmentioning

confidence: 99%

Section: Spatial Resolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

rad

Peyvandi

Sadeghi

2019

Polish Journal of Medical Physics and Engineering

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“…Yet, there are some implementations which are based on alternative geometries such as rotating [6][7][8][9] or static planar heads [10][11][12]. The incorporation of SiPM arrays provides the means to design flexible PET scanners so as to improve sensitivity, with limited number of detector heads [13].…”

Section: Introductionmentioning

confidence: 99%

Design considerations for a C-shaped PET system, dedicated to small animal brain imaging, using GATE Monte Carlo simulations

Efthimiou

Papadimitroulas

Κωστού

et al. 2015

J. Phys.: Conf. Ser.

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E-mail: efthimioun@upatras.grAbstract. Commercial clinical and preclinical PET scanners rely on the full cylindrical geometry for whole body scans as well as for dedicated organs. In this study we propose the construction of a low cost dual-head C-shaped PET system dedicated for small animal brain imaging. Monte Carlo simulation studies were performed using GATE toolkit to evaluate the optimum design in terms of sensitivity, distortions in the FOV and spatial resolution. The PET model is based on SiPMs and BGO pixelated arrays. Four different configurations with Cangle 0 ο , 15 ο , 30 ο and 45 ο within the modules, were considered. Geometrical phantoms were used for the evaluation process. STIR software, extended by an efficient multi-threaded ray tracing technique, was used for the image reconstruction. The algorithm automatically adjusts the size of the FOV according to the shape of the detector's geometry. The results showed improvement in sensitivity of ~15% in case of 45 ο C-angle compared to the 0 ο case. The spatial resolution was found 2 mm for 45° C-angle.

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Precise prediction of radiation interaction position in plastic rod scintillators using a fast and simple technique: Artificial neural network

Peyvandi

rad

2018

Nuclear Engineering and Technology

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Design and performance evaluation of a high resolution IRI-microPET preclinical scanner

Cited by 6 publications

References 29 publications

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

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

Design considerations for a C-shaped PET system, dedicated to small animal brain imaging, using GATE Monte Carlo simulations

Precise prediction of radiation interaction position in plastic rod scintillators using a fast and simple technique: Artificial neural network

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