Low-Dose CT with Deep Learning Regularization via Proximal Forward Backward Splitting

Autor:	Ding, Qiaoqiao, Chen, Gaoyu, Zhang, Xiaoqun, Huang, Qiu, Gao, Hui Jiand Hao
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
DOI:	10.1088/1361-6560/ab831a
Popis:	Low dose X-ray computed tomography (LDCT) is desirable for reduced patient dose. This work develops image reconstruction methods with deep learning (DL) regularization for LDCT. Our methods are based on unrolling of proximal forward-backward splitting (PFBS) framework with data-driven image regularization via deep neural networks. In contrast with PFBS-IR that utilizes standard data fidelity updates via iterative reconstruction (IR) method, PFBS-AIR involves preconditioned data fidelity updates that fuse analytical reconstruction (AR) method and IR in a synergistic way, I.e. fused analytical and iterative reconstruction (AIR). The results suggest that DL-regularized methods (PFBS-IR and PFBS-AIR) provided better reconstruction quality from conventional wisdoms (AR or IR), and DL-based postprocessing method (FBPConvNet). In addition, owing to AIR, PFBS-AIR noticeably outperformed PFBS-IR. Comment: 8pages 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1909.09773 Zobrazit plný text záznamu View this record from Arxiv