  Medical image super-resolution (SR) is an active research area that has many
potential applications, including reducing scan time, bettering visual
understanding, increasing robustness in downstream tasks, etc. However,
applying deep-learning-based SR approaches for clinical applications often
encounters issues of domain inconsistency, as the test data may be acquired by
different machines or on different organs. In this work, we present a novel
algorithm called domain adaptable volumetric super-resolution (DA-VSR) to
better bridge the domain inconsistency gap. DA-VSR uses a unified feature
extraction backbone and a series of network heads to improve image quality over
different planes. Furthermore, DA-VSR leverages the in-plane and through-plane
resolution differences on the test data to achieve a self-learned domain
adaptation. As such, DA-VSR combines the advantages of a strong feature
generator learned through supervised training and the ability to tune to the
idiosyncrasies of the test volumes through unsupervised learning. Through
experiments, we demonstrate that DA-VSR significantly improves super-resolution
quality across numerous datasets of different domains, thereby taking a further
step toward real clinical applications.
