Graph convolutional networks enable fast hemorrhagic stroke monitoring with electrical impedance tomography

TL;DR

This paper introduces a graph convolutional network-based post-processing method that significantly accelerates hemorrhagic stroke imaging with electrical impedance tomography, achieving high-quality results comparable to traditional nonlinear methods.

Contribution

The study presents a novel graph U-net approach that enhances linear difference reconstructions, enabling fast, accurate 3D stroke imaging with reduced computational costs.

Findings

Post-processing improves image quality to match nonlinear methods.

Method reduces reconstruction time from hours to minutes.

Training on 2D data enables efficient 3D image processing.

Abstract

Objective: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to computationally expensive nonlinear model-based methods. Methods: A post-processing approach with graph convolutional networks is employed. Utilizing the flexibility of the graph setting, a graph U-net is trained on linear difference reconstructions from 2D simulated stroke data and applied to fully 3D images from realistic simulated and experimental data. An additional network, trained on 3D vs. 2D images, is also considered for comparison. Results: Post-processing the linear difference reconstructions through the graph U-net significantly improved the image quality, resulting in images comparable to, or better than, the time-intensive nonlinear reconstruction method (a few minutes vs. several hours). Conclusion: Pairing a fast reconstruction method, such as linear difference imaging, with post-processing through a graph U-net provided significant improvements, at a negligible computational cost. Training in the graph framework vs classic pixel-based setting (CNN) allowed the ability to train on 2D cross-sectional images and process 3D volumes providing a nearly 50x savings in data simulation costs with no noticeable loss in quality. Significance: The proposed approach of post-processing a linear difference reconstruction with the graph U-net could be a feasible approach for on-line monitoring of hemorrhagic stroke.