The medical imaging technique, e.g., positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) is essential for clinical diagnosis and nuclear medicine. However, due to the hardware limitations of scanners, it is always clinically challenging to obtain high-resolution (HR) medical images. With the development of artificial intelligence, image super-resolution has been an effective technique to enhance the spatial resolution of medical images. In this paper, we propose a novel medical image super-resolution method using a relativistic average generative adversarial network (GAN), which consists of a generator and a discriminator for enhancing medical imaging quality in terms of both numerical criteria and visual results. The generator is trained to reconstruct HR images according to low-resolution (LR) counterparts. In contrast, the discriminator is trained to discriminate the probability of whether real HR images are more realistic than reconstructed images, further enhancing visual results. We apply our proposed method to two different public medical datasets, and experimental results show that our proposed method outperforms in terms of visual results, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), model complexity and an additional non-reference image quality assessment metric, compared with other state-of-the-art medical image super-resolution methods.

医学成像技术，例如正电子发射断层扫描（PET），X射线计算机断层扫描（CT）和磁共振成像（MRI）对于临床诊断和核医学至关重要。但是，由于扫描仪的硬件限制，获得高分辨率（HR）医学图像始终在临床上具有挑战性。随着人工智能的发展，图像超分辨率已经成为提高医学图像空间分辨率的有效技术。在本文中，我们提出了一种使用相对论平均生成对抗网络（GAN）的新颖医学图像超分辨率方法，该方法由生成器和鉴别器组成，可从数值标准和视觉效果两方面提高医学成像质量。训练生成器以根据低分辨率（LR）副本重建HR图像。相比之下，训练鉴别器以鉴别实际HR图像是否比重建图像更真实的可能性，从而进一步增强视觉效果。我们将我们提出的方法应用于两个不同的公共医疗数据集，实验结果表明，我们提出的方法在视觉结果，峰信噪比（PSNR），结构相似性指数（SSIM），模型复杂度以及其他方面均优于非参考图像质量评估指标，与其他最新医学图像超分辨率方法相比。进一步增强视觉效果。我们将我们提出的方法应用于两个不同的公共医疗数据集，实验结果表明，我们提出的方法在视觉结果，峰信噪比（PSNR），结构相似性指数（SSIM），模型复杂度以及其他方面均优于非参考图像质量评估指标，与其他最新医学图像超分辨率方法相比。进一步增强视觉效果。我们将我们提出的方法应用于两个不同的公共医疗数据集，实验结果表明，我们提出的方法在视觉结果，峰信噪比（PSNR），结构相似性指数（SSIM），模型复杂度以及其他方面均优于非参考图像质量评估指标，与其他最新医学图像超分辨率方法相比。