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, aiming 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 in total. 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.

翻译：超分辨率在医学成像中扮演着关键角色，因为它提供了一种在不增加额外采集成本的情况下实现高空间分辨率和图像质量的替代方案。过去几十年间，深度神经网络的快速发展通过新型网络架构、损失函数和评估指标推动了超分辨率性能的提升。具体而言，视觉Transformer在广泛的计算机视觉任务中占据主导地位，但在将其应用于低级医学图像处理任务时仍存在挑战。本文提出了一种高效的视觉Transformer，结合了残差密集连接和局部特征融合，旨在实现医学模态的高效单图像超分辨率（SISR）。此外，我们实现了一种带有手动控制的通用感知损失，通过融入医学图像分割的先验知识来改善期望方面的图像质量。与四种公开医学图像数据集上的最新方法相比，所提方法在总共七种模态中的六种模态上取得了最佳PSNR分数，平均PSNR提升+0.09 dB，而参数仅为SwinIR的38%。另一方面，基于分割的感知损失使包括CNN和视觉Transformer在内的SOTA方法平均PSNR提升了+0.14 dB。此外，我们进行了全面的消融研究，探讨了视觉Transformer优于CNN的潜在因素以及网络与损失函数组件的影响。