Multi-frequency Electrical Impedance Tomography Reconstruction with Multi-Branch Attention Image Prior

TL;DR

This paper introduces an unsupervised multi-branch attention network for multi-frequency electrical impedance tomography that achieves high-quality reconstructions without training data, improving robustness and generalization over existing supervised methods.

Contribution

The paper presents a novel unsupervised learning framework using a multi-branch attention image prior for mfEIT reconstruction, eliminating the need for training data and capturing inter- and intra-frequency correlations.

Findings

Achieves comparable or better performance than state-of-the-art supervised methods.

Demonstrates robustness and superior generalization in simulations and real-world experiments.

Reduces reliance on extensive training data, enhancing practical applicability.

Abstract

Multi-frequency Electrical Impedance Tomography (mfEIT) is a promising biomedical imaging technique that estimates tissue conductivities across different frequencies. Current state-of-the-art (SOTA) algorithms, which rely on supervised learning and Multiple Measurement Vectors (MMV), require extensive training data, making them time-consuming, costly, and less practical for widespread applications. Moreover, the dependency on training data in supervised MMV methods can introduce erroneous conductivity contrasts across frequencies, posing significant concerns in biomedical applications. To address these challenges, we propose a novel unsupervised learning approach based on Multi-Branch Attention Image Prior (MAIP) for mfEIT reconstruction. Our method employs a carefully designed Multi-Branch Attention Network (MBA-Net) to represent multiple frequency-dependent conductivity images and simultaneously reconstructs mfEIT images by iteratively updating its parameters. By leveraging the implicit regularization capability of the MBA-Net, our algorithm can capture significant inter- and intra-frequency correlations, enabling robust mfEIT reconstruction without the need for training data. Through simulation and real-world experiments, our approach demonstrates performance comparable to, or better than, SOTA algorithms while exhibiting superior generalization capability. These results suggest that the MAIP-based method can be used to improve the reliability and applicability of mfEIT in various settings.