# A Learning-Based 3D EIT Image Reconstruction Method

arXiv: https://arxiv.org/abs/2208.14449 · HTML: https://mlsanity.com/paper/2208.14449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 2 of 2)

- [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https://mlsanity.com/paper/1610.07584/) (2016)
- [MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography](https://mlsanity.com/paper/2105.12474/) (2021)

## Abstract (arXiv)

Deep learning has been widely employed to solve the Electrical Impedance Tomography (EIT) image reconstruction problem. Most existing physical model-based and learning-based approaches focus on 2D EIT image reconstruction. However, when they are directly extended to the 3D domain, the reconstruction performance in terms of image quality and noise robustness is hardly guaranteed mainly due to the significant increase in dimensionality. This paper presents a learning-based approach for 3D EIT image reconstruction, which is named Transposed convolution with Neurons Network (TN-Net). Simulation and experimental results show the superior performance and generalization ability of TN-Net compared with prevailing 3D EIT image reconstruction algorithms.