Ningning Liang / Information Engineering University
Ailong Cai / Information Engineering University
Lei Li / Information Engineering University
Minghua Sun / Henan Provincial People's Hospital
Bin Yan / Information Engineering University
Dual-energy computed tomography (DECT) has high application prospects in distinguishing and quantifying materials. However, DECT requires at least two full-angle scans at different energies. In this paper, in order to reduce the radiation dose, we use the plug-and-play (PnP) framework to obtain high-quality decomposed materials under sparse angle scanning. Specifically, we design the PnP framework as a combination of the FFDnet denoiser prior and the least square estimation model to suppress artifacts. To verify the applicability of the proposed method in the clinical environment, we simulated the chest cavity for experiments. The results show that the proposed methods can achieve high-quality basis material decomposition under different sparse views.