  Dose reduction in computed tomography (CT) is essential for decreasing
radiation risk in clinical applications. Iterative reconstruction is one of the
most promising ways to compensate for the increased noise due to reduction of
photon flux. Rather than most existing prior-driven algorithms that benefit
from manually designed prior functions or supervised learning schemes, in this
work we integrate the data-consistency as a conditional term into the iterative
generative model for low-dose CT. At the stage of prior learning, the gradient
of data density is directly learned from normal-dose CT images as a prior. Then
at the iterative reconstruction stage, the stochastic gradient descent is
employed to update the trained prior with annealed and conditional schemes. The
distance between the reconstructed image and the manifold is minimized along
with data fidelity during reconstruction. Experimental comparisons demonstrated
the noise reduction and detail preservation abilities of the proposed method.
