Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deriving Neural Network Architectures using Precision Learning:
  Parallel-to-fan beam Conversion   In this paper, we derive a neural network architecture based on an analytical
formulation of the parallel-to-fan beam conversion problem following the
concept of precision learning. The network allows to learn the unknown
operators in this conversion in a data-driven manner avoiding interpolation and
potential loss of resolution. Integration of known operators results in a small
number of trainable parameters that can be estimated from synthetic data only.
The concept is evaluated in the context of Hybrid MRI/X-ray imaging where
transformation of the parallel-beam MRI projections to fan-beam X-ray
projections is required. The proposed method is compared to a traditional
rebinning method. The results demonstrate that the proposed method is superior
to ray-by-ray interpolation and is able to deliver sharper images using the
same amount of parallel-beam input projections which is crucial for
interventional applications. We believe that this approach forms a basis for
further work uniting deep learning, signal processing, physics, and traditional
pattern recognition.

11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex