Nuclear Engineering and Technology

  • 제53권6호
  • /
  • Pages.1947-1954
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  • 2021
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  • 1738-5733(pISSN)
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  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Similarity analysis of pixelated CdTe semiconductor gamma camera image using a quadrant bar phantom for nuclear medicine: Monte Carlo simulation study

  • Park, Chan Rok (Department of Radiological Science, Jeonju University) ;
  • Kang, Seong-Hyeon (Department of Radiological Science, Gachon University) ;
  • Lee, Youngjin (Department of Radiological Science, Gachon University)
  • 투고 : 2020.06.15
  • 심사 : 2020.12.09
  • 발행 : 2021.06.25
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초록

In the nuclear medicine imaging, quality control (QC) process using quadrant bar phantom is fundamental aspect of evaluating the spatial resolution. In addition, QC process of gamma camera is performed by daily or weekly. Recently, Monte Carlo simulation using the Geant4 application for tomographic emission (GATE) is widely applied in the pre-clinical nuclear medicine field for modeling gamma cameras with pixelated cadmium telluride (CdTe) semiconductor detector. In this study, we modeled a pixelated CdTe semiconductor detector and quadrant bar phantom (0.5, 1.0, 1.5, and 2.0 mm bar thicknesses) using the GATE tool. Similarity analysis based on correlation coefficients and peak signal-to-noise ratios was performed to compare image qualities for various source to collimator distances (0, 2, 4, 6, and 8 cm) and collimator lengths (0.2, 0.4, 0.6, 0.8, and 1.0 cm). To this end, we selected reference images based on collimator length and source to collimator distance settings. The results demonstrate that as the collimator length increases and the source to collimator distance decreases, the similarity to reference images improves. Therefore, our simulation results represent valuable information for the modeling of CdTe-based semiconductor gamma imaging systems and QC phantoms in the field of nuclear medicine.

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과제정보

This research was supported by the National Research Foundation of Korea funded by the Ministry of Science and ICT (No. NRF-2019R1F1A1062811).

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