Single image super-resolution (SISR) aims to obtain a high-resolution output from one low-resolution image. Currently, deep learning-based SISR approaches have been widely discussed in medical image processing, because of their potential to achieve high-quality, high spatial resolution images without the cost of additional scans. However, most existing methods are designed for scale-specific SR tasks and are unable to generalize over magnification scales. In this paper, we propose an approach for medical image arbitrary-scale super-resolution (MIASSR), in which we couple meta-learning with generative adversarial networks (GANs) to super-resolve medical images at any scale of magnification in (1, 4]. Compared to state-of-the-art SISR algorithms on single-modal magnetic resonance (MR) brain images (OASIS-brains) and multi-modal MR brain images (BraTS), MIASSR achieves comparable fidelity performance and the best perceptual quality with the smallest model size. We also employ transfer learning to enable MIASSR to tackle SR tasks of new medical modalities, such as cardiac MR images (ACDC) and chest computed tomography images (COVID-CT). The source code of our work is also public. Thus, MIASSR has the potential to become a new foundational pre-/post-processing step in clinical image analysis tasks such as reconstruction, image quality enhancement, and segmentation.

中文翻译：

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医学图像的任意尺度超分辨率

单图像超分辨率（SISR）旨在从一幅低分辨率图像获得高分辨率输出。目前，基于深度学习的 SISR 方法已在医学图像处理领域得到广泛讨论，因为它们有潜力获得高质量、高空间分辨率的图像，而无需额外的扫描成本。然而，大多数现有方法都是针对特定尺度的 SR 任务而设计的，无法推广到放大尺度。在本文中，我们提出了一种医学图像任意尺度超分辨率（MIASSR）的方法，其中我们将元学习与生成对抗网络（GAN）结合起来，以任意放大倍数超分辨率医学图像。（1, 4]。与单模态磁共振 (MR) 脑图像 (OASIS-brains) 和多模态 MR 脑图像 (BraTS) 上最先进的 SISR 算法相比，MIASSR 实现了可比的保真度性能和最佳感知质量。最小模型尺寸。我们还采用迁移学习，使 MIASSR 能够处理新医疗模式的 SR 任务，例如心脏 MR 图像 (ACDC) 和胸部计算机断层扫描图像 (COVID-CT)。我们工作的源代码也是公开的。因此，MIASSR 有潜力成为临床图像分析任务（例如重建、图像质量增强和分割）中新的基础前/后处理步骤。