  X-ray computed tomography (CT) is widely used in clinical practice. The
involved ionizing X-ray radiation, however, could increase cancer risk. Hence,
the reduction of the radiation dose has been an important topic in recent
years. Few-view CT image reconstruction is one of the main ways to minimize
radiation dose and potentially allow a stationary CT architecture. In this
paper, we propose a deep encoder-decoder adversarial reconstruction (DEAR)
network for 3D CT image reconstruction from few-view data. Since the artifacts
caused by few-view reconstruction appear in 3D instead of 2D geometry, a 3D
deep network has a great potential for improving the image quality in a
data-driven fashion. More specifically, our proposed DEAR-3D network aims at
reconstructing 3D volume directly from clinical 3D spiral cone-beam image data.
DEAR is validated on a publicly available abdominal CT dataset prepared and
authorized by Mayo Clinic. Compared with other 2D deep-learning methods, the
proposed DEAR-3D network can utilize 3D information to produce promising
reconstruction results.
