# Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data

arXiv: https://arxiv.org/abs/1911.05880 · HTML: https://mlsanity.com/paper/1911.05880/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review](https://mlsanity.com/paper/2403.18565/) (2024)
- [3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions](https://mlsanity.com/paper/2105.14130/) (2021)
- [Segmentation-free PVC for Cardiac SPECT using a Densely-connected Multi-dimensional Dynamic Network](https://mlsanity.com/paper/2206.12344/) (2022)
- [A Novel Implicit Neural Representation for Volume Data](https://mlsanity.com/paper/2403.08566/) (2024)

## Abstract (arXiv)

X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dose has been an important topic in recent years. Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper, we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT image reconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in 3D instead of 2D geometry, a 3D deep network has a great potential for improving the image quality in a data-driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing 3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publicly available abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other 2D deep-learning methods, the proposed DEAR-3D network can utilize 3D information to produce promising reconstruction results.