  Super-resolution plays an essential role in medical imaging because it
provides an alternative way to achieve high spatial resolutions and image
quality with no extra acquisition costs. In the past few decades, the rapid
development of deep neural networks has promoted super-resolution performance
with novel network architectures, loss functions and evaluation metrics.
Specifically, vision transformers dominate a broad range of computer vision
tasks, but challenges still exist when applying them to low-level medical image
processing tasks. This paper proposes an efficient vision transformer with
residual dense connections and local feature fusion to achieve efficient
single-image super-resolution (SISR) of medical modalities. Moreover, we
implement a general-purpose perceptual loss with manual control for image
quality improvements of desired aspects by incorporating prior knowledge of
medical image segmentation. Compared with state-of-the-art methods on four
public medical image datasets, the proposed method achieves the best PSNR
scores of 6 modalities among seven modalities. It leads to an average
improvement of $+0.09$ dB PSNR with only 38\% parameters of SwinIR. On the
other hand, the segmentation-based perceptual loss increases $+0.14$ dB PSNR on
average for SOTA methods, including CNNs and vision transformers. Additionally,
we conduct comprehensive ablation studies to discuss potential factors for the
superior performance of vision transformers over CNNs and the impacts of
network and loss function components. The code will be released on GitHub with
the paper published.
