  In clinical practice, anisotropic volumetric medical images with low
through-plane resolution are commonly used due to short acquisition time and
lower storage cost. Nevertheless, the coarse resolution may lead to
difficulties in medical diagnosis by either physicians or computer-aided
diagnosis algorithms. Deep learning-based volumetric super-resolution (SR)
methods are feasible ways to improve resolution, with convolutional neural
networks (CNN) at their core. Despite recent progress, these methods are
limited by inherent properties of convolution operators, which ignore content
relevance and cannot effectively model long-range dependencies. In addition,
most of the existing methods use pseudo-paired volumes for training and
evaluation, where pseudo low-resolution (LR) volumes are generated by a simple
degradation of their high-resolution (HR) counterparts. However, the domain gap
between pseudo- and real-LR volumes leads to the poor performance of these
methods in practice. In this paper, we build the first public real-paired
dataset RPLHR-CT as a benchmark for volumetric SR, and provide baseline results
by re-implementing four state-of-the-art CNN-based methods. Considering the
inherent shortcoming of CNN, we also propose a transformer volumetric
super-resolution network (TVSRN) based on attention mechanisms, dispensing with
convolutions entirely. This is the first research to use a pure transformer for
CT volumetric SR. The experimental results show that TVSRN significantly
outperforms all baselines on both PSNR and SSIM. Moreover, the TVSRN method
achieves a better trade-off between the image quality, the number of
parameters, and the running time. Data and code are available at
https://github.com/smilenaxx/RPLHR-CT.
