# Data-Efficient Limited-Angle CT Using Deep Priors and Regularization

arXiv: https://arxiv.org/abs/2502.12293 · HTML: https://mlsanity.com/paper/2502.12293/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods](https://mlsanity.com/paper/2003.04989/) (2020)
- [TorchRadon: Fast Differentiable Routines for Computed Tomography](https://mlsanity.com/paper/2009.14788/) (2020)
- [Early Stopping for Deep Image Prior](https://mlsanity.com/paper/2112.06074/) (2021)
- [Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network](https://mlsanity.com/paper/1607.08707/) (2016)
- [Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning](https://mlsanity.com/paper/1711.00414/) (2017)
- [Total Variation Applications in Computer Vision](https://mlsanity.com/paper/1603.09599/) (2016)
- [Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning](https://mlsanity.com/paper/2001.02469/) (2020)
- [Extreme Few-view CT Reconstruction using Deep Inference](https://mlsanity.com/paper/1910.05375/) (2019)
- [Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data](https://mlsanity.com/paper/2309.06948/) (2023)

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

- [Limited-Angle Tomography Reconstruction via Projector Guided 3D Diffusion](https://mlsanity.com/paper/2510.06516/) (2025)

## Abstract (arXiv)

Reconstructing an image from its Radon transform is a fundamental computed tomography (CT) task arising in applications such as X-ray scans. In many practical scenarios, a full 180-degree scan is not feasible, or there is a desire to reduce radiation exposure. In these limited-angle settings, the problem becomes ill-posed, and methods designed for full-view data often leave significant artifacts. We propose a very low-data approach to reconstruct the original image from its Radon transform under severe angle limitations. Because the inverse problem is ill-posed, we combine multiple regularization methods, including Total Variation, a sinogram filter, Deep Image Prior, and a patch-level autoencoder. We use a differentiable implementation of the Radon transform, which allows us to use gradient-based techniques to solve the inverse problem. Our method is evaluated on a dataset from the Helsinki Tomography Challenge 2022, where the goal is to reconstruct a binary disk from its limited-angle sinogram. We only use a total of 12 data points--eight for learning a prior and four for hyperparameter selection--and achieve results comparable to the best synthetic data-driven approaches.