Because single-photon emission computed tomography (SPECT) is one of the widely used nuclear medicine imaging systems, it is extremely important to acquire high-quality images for diagnosis. In this study, we designed a super-resolution (SR) technique using dense block-based deep convolutional neural network (CNN) and evaluated the algorithm on real SPECT phantom images. To acquire the phantom images, a real SPECT system using a^99mTc source and two physical phantoms was used. To confirm the image quality, the noise properties and visual quality metric evaluation parameters were calculated. The results demonstrate that our proposed method delivers a more valid SR improvement by using dense block-based deep CNNs as compared to conventional reconstruction techniques. In particular, when the proposed method was used, the quantitative performance was improved from 1.2 to 5.0 times compared to the result of using the conventional iterative reconstruction. Here, we confirmed the effects on the image quality of the resulting SR image, and our proposed technique was shown to be effective for nuclear medicine imaging.

由于单光子发射计算机断层扫描 (SPECT) 是广泛使用的核医学成像系统之一，因此获取高质量的图像进行诊断极为重要。在这项研究中，我们使用基于密集块的深度卷积神经网络 (CNN) 设计了一种超分辨率 (SR) 技术，并在真实的 SPECT 幻影图像上评估了该算法。为获取幻像图像，使用^99m的真实 SPECT 系统使用了 Tc 源和两个物理模型。为了确认图像质量，计算了噪声特性和视觉质量度量评价参数。结果表明，与传统重建技术相比，我们提出的方法通过使用基于密集块的深度 CNN 提供了更有效的 SR 改进。特别是，当使用所提出的方法时，与使用传统迭代重建的结果相比，量化性能从 1.2 倍提高到 5.0 倍。在这里，我们确认了对生成的 SR 图像的图像质量的影响，并且我们提出的技术被证明对核医学成像是有效的。