# Bound-Constrained Sparse Representation for Electrical Impedance Tomography

arXiv: https://arxiv.org/abs/2605.28392 · HTML: https://mlsanity.com/paper/2605.28392/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 39 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 1 of 1)

- [Physics-Driven Neural Compensation For Electrical Impedance Tomography](https://mlsanity.com/paper/2504.18067/) (2025)

## Abstract (arXiv)

This study proposes a bound-constrained sparse representation (BC-SR) framework for electrical impedance tomography (EIT), aimed at improving conductivity estimation without explicit regularization. BC-SR adopts a representation-driven strategy, generating conductivity from low-dimensional latent variables via an implicit composite parameterization. Structural priors are embedded using a truncated graph-Laplacian basis, while a bound-preserving nonlinear mapping enforces admissible conductivity ranges and improves conditioning through implicit gradient modulation. The approach ensures robust convergence, even under noisy or incomplete data. Extensive validation on 2D/3D simulations, tank experiments, and in-vivo lung data shows that BC-SR improves physical consistency and structural fidelity, offering enhanced robustness compared to traditional methods. Additionally, BC-SR enables 3D time-difference EIT reconstruction, offering improved spatial resolution and a more coherent representation of 3D conductivity distributions, particularly for in-vivo lung data. This suggests potential for improved performance in EIT, particularly in clinical applications for respiratory monitoring.