  Deep learning-based single image super-resolution (SISR) methods face various
challenges when applied to 3D medical volumetric data (i.e., CT and MR images)
due to the high memory cost and anisotropic resolution, which adversely affect
their performance. Furthermore, mainstream SISR methods are designed to work
over specific upsampling factors, which makes them ineffective in clinical
practice. In this paper, we introduce a Spatially Aware Interpolation NeTwork
(SAINT) for medical slice synthesis to alleviate the memory constraint that
volumetric data poses. Compared to other super-resolution methods, SAINT
utilizes voxel spacing information to provide desirable levels of details, and
allows for the upsampling factor to be determined on the fly. Our evaluations
based on 853 CT scans from four datasets that contain liver, colon, hepatic
vessels, and kidneys show that SAINT consistently outperforms other SISR
methods in terms of medical slice synthesis quality, while using only a single
model to deal with different upsampling factors.
