  Low dose CT is of great interest in these days. Dose reduction raises noise
level in projections and decrease image quality in reconstructions. Model based
image reconstruction can combine statistical noise model together with prior
knowledge into an Bayesian optimization problem so that significantly reduce
noise and artefacts. In this work, we propose a model-base deep learning for CT
reconstruction so that a reconstruction network can be trained with no
ground-truth images needed. Instead of minimizing cost function for each image,
the network learns to minimize an ensemble cost function for the whole training
set. No iteration will be needed for real data reconstruction using such a
trained network. We experimented with a penalized weighted least-squares (PWLS)
cost function for low dose CT reconstruction and tested on data from a
practical dental CT. Very encouraging results with great noise reductions are
obtained.
