  Computed Tomography (CT) is an advanced imaging technology used in many
important applications. Here we present a deep-learning (DL) based CT
super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms
into high resolution (HR) CT images. The method synergistically combines a SR
model in sinogram domain, a deblur model in image domain, and the iterative
framework into a CT SR algorithm super resolution and deblur based iterative
reconstruction (SADIR). We incorporated the CT domain knowledge into the SADIR
and unrolled it into a DL network (SADIR Net). The SADIR Net is a zero shot
learning (ZSL) network, which can be trained and tested with a single sinogram
in the test time. The SADIR was evaluated via SR CT imaging of a Catphan700
physical phantom and a biological ham, and its performance was compared to the
other state of the art (SotA) DL-based methods. The results show that the
zero-shot SADIR-Net can indeed provide a performance comparable to the other
SotA methods for CT SR reconstruction, especially in situations where training
data is limited. The SADIR method can find use in improving CT resolution
beyond hardware limits or lowering requirement on CT hardware.
