  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.
