  Computed tomography (CT) is a widely used non-invasive medical imaging
technique for disease diagnosis. The diagnostic accuracy is often affected by
image resolution, which can be insufficient in practice. For medical CT images,
the through-plane resolution is often worse than the in-plane resolution and
there can be overlap between slices, causing difficulties in diagnoses.
Self-supervised methods for through-plane resolution enhancement, which train
on in-plane images and infer on through-plane images, have shown promise for
both CT and MRI imaging. However, existing self-supervised methods either
neglect overlap or can only handle specific cases with fixed combinations of
resolution and overlap. To address these limitations, we propose a
self-supervised method called SR4ZCT. It employs the same off-axis training
approach while being capable of handling arbitrary combinations of resolution
and overlap. Our method explicitly models the relationship between resolutions
and voxel spacings of different planes to accurately simulate training images
that match the original through-plane images. We highlight the significance of
accurate modeling in self-supervised off-axis training and demonstrate the
effectiveness of SR4ZCT using a real-world dataset.
