Work

Nonlinear multi-frequency electrical impedance tomography: An unfolded optimization approach

Year: 2026

Type: article

Abstract: Multi-frequency Electrical Impedance Tomography (mfEIT) represents a promising biomedical imaging modality that enables the estimation of tissue conductivities across a range of frequencies. Addressing this challenge, we present a novel variational network, a model-based learning paradigm that strategically merges the advantages and interpretability of classical iterative reconstruction with the power of deep learning. This approach integrates graph neural networks (GNNs) within the iterative Proximal Regularized Gauss Newton (PRGN) framework. By unfolding the PRGN algorithm, where each iteration corresponds to a network layer, we leverage the physical insights of nonlinear model fitting alongside the GNN’s capacity to capture inter-frequency correlations. Notably, the GNN architecture preserves the irregular triangular mesh structure used in the solution of the nonlinear forward model, enabling accurate reconstruction of overlapping tissue fraction concentrations.

Source: Journal of Computational Physics

DOI: 10.1016/j.jcp.2026.115293

Authors: Giovanni S. Alberti, Damiana Lazzaro, Serena Morigi, Luca Ratti, Matteo Santacesaria

Institutions: University of Genoa, University of Bologna

Language: English

Topic: Electrical and Bioimpedance Tomography

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,980

Funders: European Research Council, Air Force Office of Scientific Research, Ministero dell'Istruzione e del Merito