Geometric Constraints Enable Self-Supervised Sinogram Inpainting in
Sparse-View Tomography
- URL: http://arxiv.org/abs/2302.06436v2
- Date: Wed, 9 Aug 2023 13:19:29 GMT
- Title: Geometric Constraints Enable Self-Supervised Sinogram Inpainting in
Sparse-View Tomography
- Authors: Fabian Wagner, Mareike Thies, Noah Maul, Laura Pfaff, Oliver Aust,
Sabrina Pechmann, Christopher Syben, Andreas Maier
- Abstract summary: Sparse-angle tomographic scans reduce radiation and accelerate data acquisition, but suffer from image artifacts and noise.
Existing image processing algorithms can restore CT reconstruction quality but often require large training data sets or can not be used for truncated objects.
This work presents a self-supervised projection inpainting method that allows optimizing missing projective views via gradient-based optimization.
- Score: 7.416898042520079
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The diagnostic quality of computed tomography (CT) scans is usually
restricted by the induced patient dose, scan speed, and image quality.
Sparse-angle tomographic scans reduce radiation exposure and accelerate data
acquisition, but suffer from image artifacts and noise. Existing image
processing algorithms can restore CT reconstruction quality but often require
large training data sets or can not be used for truncated objects. This work
presents a self-supervised projection inpainting method that allows optimizing
missing projective views via gradient-based optimization. By reconstructing
independent stacks of projection data, a self-supervised loss is calculated in
the CT image domain and used to directly optimize projection image intensities
to match the missing tomographic views constrained by the projection geometry.
Our experiments on real X-ray microscope (XRM) tomographic mouse tibia bone
scans show that our method improves reconstructions by 3.1-7.4%/7.7-17.6% in
terms of PSNR/SSIM with respect to the interpolation baseline. Our approach is
applicable as a flexible self-supervised projection inpainting tool for
tomographic applications.
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