Gamma images inherently contain noise distributions owing to the low photon count in a detector. Several methods using semiconductor detectors have been reported to overcome this challenge. Median modified Wiener filter (MMWF) algorithm is one of the effective noise reduction methods, which employs mask size as an essential parameter for optimization. In this study, we evaluated and optimized the acquired gamma image using the proposed MMWF algorithm based on the Monte Carlo simulation tool with various mask sizes (3 × 3, 5 × 5, and 7 × 7) in a cadmium telluride pixelated semiconductor detector. Our results demonstrated that the images obtained with 7 × 7 mask size showed the lowest values in the normalized noise power spectrum. The contrast-to-noise ratio and the coefficient of variation were observed to be 29.2–65.9% better for the images obtained with the 7 × 7 mask size compared with those obtained using other mask sizes. This study suggests that an excellent image quality of gamma images can be achieved with semiconductor detectors by optimizing the mask size in the MMWF algorithm.

由于探测器中光子数量少，伽玛图像固有地包含噪声分布。已经报道了使用半导体检测器的几种方法来克服这一挑战。中值改进的维纳滤波器（MMWF）算法是一种有效的降噪方法，该算法采用掩码大小作为优化的基本参数。在这项研究中，我们在碲化镉像素化半导体检测器中使用基于蒙特卡罗模拟工具的MMWF算法（具有各种掩模尺寸（3×3、5×5和7×7））评估并优化了所采集的伽马图像。我们的结果表明，使用7×7掩模尺寸获得的图像在归一化噪声功率谱中显示出最低值。对比噪声比和变异系数为29.2–65。使用7×7掩模尺寸获得的图像比使用其他掩模尺寸获得的图像好9％。这项研究表明，通过在MMWF算法中优化掩模尺寸，可以使用半导体探测器实现出色的伽马图像质量。