  Low-dose computed tomography (LDCT) scans, which can effectively alleviate
the radiation problem, will degrade the imaging quality. In this paper, we
propose a novel LDCT reconstruction network that unrolls the iterative scheme
and performs in both image and manifold spaces. Because patch manifolds of
medical images have low-dimensional structures, we can build graphs from the
manifolds. Then, we simultaneously leverage the spatial convolution to extract
the local pixel-level features from the images and incorporate the graph
convolution to analyze the nonlocal topological features in manifold space. The
experiments show that our proposed method outperforms both the quantitative and
qualitative aspects of state-of-the-art methods. In addition, aided by a
projection loss component, our proposed method also demonstrates superior
performance for semi-supervised learning. The network can remove most noise
while maintaining the details of only 10% (40 slices) of the training data
labeled.
