Investigating the effect of rank and angle parameters on the image quality of the gamma camera at long distances

Document Type : Original Article

Authors

1 Assistant professor, science and technology center of physics, faculty of basic science, Imam Hossein university, Tehran, Iran

2 Science and technology center of physics, Faculty of basic science, Imam Hossein comprehensive university, Tehran, Iran

Abstract

Gamma-ray imaging is the most essential way to identify unknown gamma-ray emitting objects in nuclear security and safety, such as preventing nuclear terrorism. The mask parameters of a coded aperture are critical design features when optimizing the performance of a gamma-ray camera. This paper presents the results of the effect of the mask rank on the image quality of a lead-based coded-aperture imaging (CAI) with a mosaic-modified uniformly redundant array (MURA) mask using the Monte Carlo Gate code. The impact of three ranks of 5, 11, and 19 on the image quality is quantified. The imaging of a surface cesium-137 source with an activity of 0.02 mCi sitting at a distance of 3 m from the system at the three angles of 0, 25, and 45 degrees was demonstrated using maximum likelihood expectation maximization (MLEM) method. To evaluate the quality of the reconstructed images, the peak signal-to-noise ratio (SNR) of the captured gamma camera images was evaluated as a function of the mask rank and viewing angle. In order to find the most appropriate distance between the mask and the detector in the optimal performance of the gamma camera imaging system, the distance between the mask and the detector was investigated at three distances of 3, 6 and 12 cm. The results showed that the MURA mask with rank 19, which is located at a distance of 3 cm from the detector, has a higher signal-to-noise ratio and lower angular error than masks with ranks 5 and 11, and therefore it is suitable for use in gamma cameras with security application.

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History

  • Receive Date: 04 February 2024
  • Revise Date: 08 September 2024
  • Accept Date: 11 May 2024
  • Publish Date: 21 January 2024

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