# Improving Limited Angle CT Reconstruction with a Robust GAN Prior

arXiv: https://arxiv.org/abs/1910.01634 · HTML: https://mlsanity.com/paper/1910.01634/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Differentiable Forward Projector for X-ray Computed Tomography](https://mlsanity.com/paper/2307.05801/) (2023)
- [Real-Time Limited-View CT Inpainting and Reconstruction with Dual Domain Based on Spatial Information](https://mlsanity.com/paper/2101.07594/) (2021)

## Abstract (arXiv)

Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved. In this work we study how pre-trained generative adversarial networks (GANs) can be used to clean noisy, highly artifact laden reconstructions from conventional techniques, by effectively projecting onto the inferred image manifold. In particular, we use a robust version of the popularly used GAN prior for inverse problems, based on a recent technique called corruption mimicking, that significantly improves the reconstruction quality. The proposed approach operates in the image space directly, as a result of which it does not need to be trained or require access to the measurement model, is scanner agnostic, and can work over a wide range of sensing scenarios.