# Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography

arXiv: https://arxiv.org/abs/2302.06436 · HTML: https://mlsanity.com/paper/2302.06436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [PYRO-NN: Python Reconstruction Operators in Neural Networks](https://mlsanity.com/paper/1904.13342/) (2019)
- [Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior](https://mlsanity.com/paper/1908.06792/) (2019)
- [2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network](https://mlsanity.com/paper/2012.04743/) (2020)
- [Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomography](https://mlsanity.com/paper/2201.10345/) (2022)
- [On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting](https://mlsanity.com/paper/2211.01111/) (2022)
- [Noise2Context: Context-assisted Learning 3D Thin-layer Low Dose CT Without Clean Data](https://mlsanity.com/paper/2011.12525/) (2020)
- [Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2201.07562/) (2022)

## Built on by (most cited first, 1 of 1)

- [FCDM: A Physics-Guided Bidirectional Frequency Aware Convolution and Diffusion-Based Model for Sinogram Inpainting](https://mlsanity.com/paper/2409.06714/) (2024)

## Abstract (arXiv)

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.