Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A deep convolutional neural network using directional wavelets for
  low-dose X-ray CT reconstruction   Due to the potential risk of inducing cancers, radiation dose of X-ray CT
should be reduced for routine patient scanning. However, in low-dose X-ray CT,
severe artifacts usually occur due to photon starvation, beamhardening, etc,
which decrease the reliability of diagnosis. Thus, high quality reconstruction
from low-dose X-ray CT data has become one of the important research topics in
CT community. Conventional model-based denoising approaches are, however,
computationally very expensive, and image domain denoising approaches hardly
deal with CT specific noise patterns. To address these issues, we propose an
algorithm using a deep convolutional neural network (CNN), which is applied to
wavelet transform coefficients of low-dose CT images. Specifically, by using a
directional wavelet transform for extracting directional component of artifacts
and exploiting the intra- and inter-band correlations, our deep network can
effectively suppress CT specific noises. Moreover, our CNN is designed to have
various types of residual learning architecture for faster network training and
better denoising. Experimental results confirm that the proposed algorithm
effectively removes complex noise patterns of CT images, originated from the
reduced X-ray dose. In addition, we show that wavelet domain CNN is efficient
in removing the noises from low-dose CT compared to an image domain CNN. Our
results were rigorously evaluated by several radiologists and won the second
place award in 2016 AAPM Low-Dose CT Grand Challenge. To the best of our
knowledge, this work is the first deep learning architecture for low-dose CT
reconstruction that has been rigorously evaluated and proven for its efficacy.

27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con