Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep
  Learning   Filtered back projection (FBP) is the most widely used method for image
reconstruction in X-ray computed tomography (CT) scanners. The presence of
hyper-dense materials in a scene, such as metals, can strongly attenuate
X-rays, producing severe streaking artifacts in the reconstruction. These metal
artifacts can greatly limit subsequent object delineation and information
extraction from the images, restricting their diagnostic value. This problem is
particularly acute in the security domain, where there is great heterogeneity
in the objects that can appear in a scene, highly accurate decisions must be
made quickly. The standard practical approaches to reducing metal artifacts in
CT imagery are either simplistic non-adaptive interpolation-based projection
data completion methods or direct image post-processing methods. These standard
approaches have had limited success. Motivated primarily by security
applications, we present a new deep-learning-based metal artifact reduction
(MAR) approach that tackles the problem in the projection data domain. We treat
the projection data corresponding to metal objects as missing data and train an
adversarial deep network to complete the missing data in the projection domain.
The subsequent complete projection data is then used with FBP to reconstruct
image intended to be free of artifacts. This new approach results in an
end-to-end MAR algorithm that is computationally efficient so practical and
fits well into existing CT workflows allowing easy adoption in existing
scanners. Training deep networks can be challenging, and another contribution
of our work is to demonstrate that training data generated using an accurate
X-ray simulation can be used to successfully train the deep network when
combined with transfer learning using limited real data sets. We demonstrate
the effectiveness and potential of our algorithm on simulated and real
examples.

8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex