  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 generalise 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.
