Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dual-energy CT imaging from single-energy CT data with material
  decomposition convolutional neural network   Dual-energy computed tomography (DECT) is of great significance for clinical
practice due to its huge potential to provide material-specific information.
However, DECT scanners are usually more expensive than standard single-energy
CT (SECT) scanners and thus are less accessible to undeveloped regions. In this
paper, we show that the energy-domain correlation and anatomical consistency
between standard DECT images can be harnessed by a deep learning model to
provide high-performance DECT imaging from fully-sampled low-energy data
together with single-view high-energy data, which can be obtained by using a
scout-view high-energy image. We demonstrate the feasibility of the approach
with contrast-enhanced DECT scans from 5,753 slices of images of twenty-two
patients and show its superior performance on DECT applications. The deep
learning-based approach could be useful to further significantly reduce the
radiation dose of current premium DECT scanners and has the potential to
simplify the hardware of DECT imaging systems and to enable DECT imaging using
standard SECT scanners.

29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin