Quality-guided deep reinforcement learning for parameter tuning in iterative CT reconstruction

Abstract

Tuning parameters in a reconstruction model is of central importance to iterative CT reconstruction, since it critically affects the resulting image quality. Manual parameter tuning is not only tedious, but becomes impractical when there exits a number of parameters. In this paper, we develop a novel deep reinforcement learning (DRL) framework to train a parameter-tuning policy network (PTPN) to automatically adjust parameters in a human-like manner. A quality assessment network (QAN) is trained together with PTPN to learn how to judge CT image quality, serving as a reward function to guide the reinforcement learning. We demonstrate our idea in an iterative CT reconstruction problem with pixel-wise total-variation regularization. Experimental results demonstrates the effectiveness of both PTPN and QAN, in terms of tuning parameter and evaluating image quality, respectively.