  By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT
reconstruction is a holy grail to minimize cancer risks and genetic damages,
especially for children. With the development of medical CT technologies, the
iterative algorithms are widely used to reconstruct decent CT images from a
low-dose scan. Recently, artificial intelligence (AI) techniques have shown a
great promise in further reducing CT radiation dose to the next level. In this
paper, we demonstrate that AI-powered CT reconstruction offers diagnostic image
quality at an ultra-low-dose level comparable to that of radiography.
Specifically, here we develop a Split Unrolled Grid-like Alternative
Reconstruction (SUGAR) network, in which deep learning, physical modeling and
image prior are integrated. The reconstruction results from clinical datasets
show that excellent images can be reconstructed using SUGAR from 36
projections. This approach has a potential to change future healthcare.
