Iterative Reconstruction for Low-Dose CT Using Deep Gradient Priors of Generative Model
Zhuonan He, Yikun Zhang, Yu Guan, Bing Guan, Shanzhou Niu, Yi Zhang, Yang Chen, Qiegen Liu
Abstract
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 the 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.