Dual-domain reconstruction network for sparse-view CT

Yi Zhang; Hu Chen; Wenjun Xia; Yang Chen; Baodong Liu; Yan Liu; Huaiqiang Sun; Jiliu Zhou

doi:10.1117/12.2597801

9 September 2021 Dual-domain reconstruction network for sparse-view CT

Yi Zhang, Hu Chen, Wenjun Xia, Yang Chen, Baodong Liu, Yan Liu, Huaiqiang Sun, Jiliu Zhou

Proceedings Volume 11840, Developments in X-Ray Tomography XIII; 1184016 (2021) https://doi.org/10.1117/12.2597801
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography. Traditional compressed sensing 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.

Conference Presentation

Citation Download Citation

Yi Zhang, Hu Chen, Wenjun Xia, Yang Chen, Baodong Liu, Yan Liu, Huaiqiang Sun, and Jiliu Zhou "Dual-domain reconstruction network for sparse-view CT", Proc. SPIE 11840, Developments in X-Ray Tomography XIII, 1184016 (9 September 2021); https://doi.org/10.1117/12.2597801

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