2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual
Network
- URL: http://arxiv.org/abs/2012.04743v1
- Date: Tue, 8 Dec 2020 21:16:43 GMT
- Title: 2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual
Network
- Authors: Haoyu Wei, Florian Schiffers, Tobias W\"urfl, Daming Shen, Daniel Kim,
Aggelos K. Katsaggelos, Oliver Cossairt
- Abstract summary: We present a novel framework for sparse-view tomography by decoupling the reconstruction into two steps.
The intermediate result allows for a closed-form tomographic reconstruction with preserved details and highly reduced streak-artifacts.
Second, a refinement network, PRN, trained on the reconstructions reduces any remaining artifacts.
- Score: 14.577323946585755
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Computed tomography is widely used to examine internal structures in a
non-destructive manner. To obtain high-quality reconstructions, one typically
has to acquire a densely sampled trajectory to avoid angular undersampling.
However, many scenarios require a sparse-view measurement leading to
streak-artifacts if unaccounted for. Current methods do not make full use of
the domain-specific information, and hence fail to provide reliable
reconstructions for highly undersampled data. We present a novel framework for
sparse-view tomography by decoupling the reconstruction into two steps: First,
we overcome its ill-posedness using a super-resolution network, SIN, trained on
the sparse projections. The intermediate result allows for a closed-form
tomographic reconstruction with preserved details and highly reduced
streak-artifacts. Second, a refinement network, PRN, trained on the
reconstructions reduces any remaining artifacts. We further propose a
light-weight variant of the perceptual-loss that enhances domain-specific
information, boosting restoration accuracy. Our experiments demonstrate an
improvement over current solutions by 4 dB.
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