Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT
  from Few-view Data   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.

10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas