Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sinogram upsampling using Primal-Dual UNet for undersampled CT and
  radial MRI reconstruction   CT and MRI are two widely used clinical imaging modalities for non-invasive
diagnosis. However, both of these modalities come with certain problems. CT
uses harmful ionising radiation, and MRI suffers from slow acquisition speed.
Both problems can be tackled by undersampling, such as sparse sampling.
However, such undersampled data leads to lower resolution and introduces
artefacts. Several techniques, including deep learning based methods, have been
proposed to reconstruct such data. However, the undersampled reconstruction
problem for these two modalities was always considered as two different
problems and tackled separately by different research works. This paper
proposes a unified solution for both sparse CT and undersampled radial MRI
reconstruction, achieved by applying Fourier transform-based pre-processing on
the radial MRI and then reconstructing both modalities using sinogram
upsampling combined with filtered back-projection. The Primal-Dual network is a
deep learning based method for reconstructing sparsely-sampled CT data. This
paper introduces Primal-Dual UNet, which improves the Primal-Dual network in
terms of accuracy and reconstruction speed. The proposed method resulted in an
average SSIM of 0.932 while performing sparse CT reconstruction for fan-beam
geometry with a sparsity level of 16, achieving a statistically significant
improvement over the previous model, which resulted in 0.919. Furthermore, the
proposed model resulted in 0.903 and 0.957 average SSIM while reconstructing
undersampled brain and abdominal MRI data with an acceleration factor of 16 -
statistically significant improvements over the original model, which resulted
in 0.867 and 0.949. Finally, this paper shows that the proposed network not
only improves the overall image quality, but also improves the image quality
for the regions-of-interest; as well as generalises better in presence of a
needle.

26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn