论文丨Yi Zhang, Hu Chen*, Wenjun Xia, Yang Chen, Baodong Liu, Yan Liu, Huaiqiang Sun, and Jiliu Zhou： LEARN++: Recurrent dual-domain reconstruction network for compressed sensing CT

本文（LEARN++: Recurrent dual-domain reconstruction network for compressed sensing CT）原载IEEE Transactions on Radiation and Plasma Medical Sciences，由四川大学张意教授等科研人员创作，系四川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果，欢迎大家阅读。

Abstract:Compressed sensing (CS) computed tomography (CT) has been proven to be important for several clinical applications, such as sparse-view CT, digital tomosynthesis, and interior tomography. Traditional CS focuses on the design of handcrafted prior regularizers, which are usually image-dependent and time-consuming. Inspired by recently proposed deep learning-based CT reconstruction models, we extend the state-of-the-art LEARN model to a dual-domain version, dubbed LEARN++. Different from existing iteration unrolling methods, which only involve projection data in the data consistency layer, the proposed LEARN++ model integrates two parallel and interactive subnetworks to perform image restoration and sinogram inpainting operations on both the image and projection domains simultaneously, which can fully explore the latent relations between projection data and reconstructed images. The experimental results demonstrate that the proposed LEARN++ model achieves competitive qualitative and quantitative results compared to several state-of-the-art methods in terms of both artifact reduction and detail preservation.

Yi Zhang, Hu Chen*, Wenjun Xia, Yang Chen, Baodong Liu, Yan Liu, Huaiqiang Sun, and Jiliu Zhou. LEARN++: Recurrent dual-domain reconstruction network for compressed sensing CT. IEEE Transactions on Radiation and Plasma Medical Sciences, pp. 132-142, vol. 7, no. 2, 2023.（论文下载）