Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sinogram interpolation for sparse-view micro-CT with deep learning
  neural network   In sparse-view Computed Tomography (CT), only a small number of projection
images are taken around the object, and sinogram interpolation method has a
significant impact on final image quality. When the amount of sparsity (the
amount of missing views in sinogram data) is not high, conventional
interpolation methods have yielded good results. When the amount of sparsity is
high, more advanced sinogram interpolation methods are needed. Recently,
several deep learning (DL) based sinogram interpolation methods have been
proposed. However, those DL-based methods have mostly tested so far on computer
simulated sinogram data rather experimentally acquired sinogram data. In this
study, we developed a sinogram interpolation method for sparse-view micro-CT
based on the combination of U-Net and residual learning. We applied the method
to sinogram data obtained from sparse-view micro-CT experiments, where the
sparsity reached 90%. The interpolated sinogram by the DL neural network was
fed to FBP algorithm for reconstruction. The result shows that both RMSE and
SSIM of CT image are greatly improved. The experimental results demonstrate
that this sinogram interpolation method produce significantly better results
over standard linear interpolation methods when the sinogram data are extremely
sparse.

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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci