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Dose reduction in computed tomography (CT) is essential for decreasingradiation risk in clinical applications. Iterative reconstruction is one of themost promising ways to compensate for the increased noise due to reduction ofphoton flux. Rather than most existing prior-driven algorithms that benefitfrom manually designed prior functions or supervised learning schemes, in thiswork we integrate the data-consistency as a conditional term into the iterativegenerative model for low-dose CT. At the stage of prior learning, the gradientof data density is directly learned from normal-dose CT images as a prior. Thenat the iterative reconstruction stage, the stochastic gradient descent isemployed to update the trained prior with annealed and conditional schemes. Thedistance between the reconstructed image and the manifold is minimized alongwith data fidelity during reconstruction. Experimental comparisons demonstratedthe noise reduction and detail preservation abilities of the proposed method.

Iterative Reconstruction for Low-Dose CT using Deep Gradient Priors of Generative Model