# Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior

arXiv: https://arxiv.org/abs/1908.06792 · HTML: https://mlsanity.com/paper/1908.06792/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 12 of 12)

- [Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data](https://mlsanity.com/paper/2201.09318/) (2022)
- [DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstruction](https://mlsanity.com/paper/2404.17890/) (2024)
- [To be or not to be stable, that is the question: understanding neural networks for inverse problems](https://mlsanity.com/paper/2211.13692/) (2022)
- [WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer](https://mlsanity.com/paper/2207.00400/) (2022)
- [Physics-assisted Generative Adversarial Network for X-Ray Tomography](https://mlsanity.com/paper/2204.03703/) (2022)
- [Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction](https://mlsanity.com/paper/2410.10836/) (2024)
- [Advantage of Machine Learning over Maximum Likelihood in Limited-Angle Low-Photon X-Ray Tomography](https://mlsanity.com/paper/2111.08011/) (2021)
- [Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography](https://mlsanity.com/paper/2302.06436/) (2023)
- [Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2506.13545/) (2025)
- [Soft Tuy-Completeness for Robust Projection Selection in Cone-Beam CT](https://mlsanity.com/paper/2605.24023/) (2026)
- [CNN-based regularisation for CT image reconstructions](https://mlsanity.com/paper/2201.09132/) (2022)
- [Robust Visual Tracking by Motion Analyzing](https://mlsanity.com/paper/2309.03247/) (2023)

## Abstract (arXiv)

Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise. Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120-degree cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method.