BREIT: A Framework for Brain Stroke Reconstruction using Multi-Frequency 3D EIT
For researchers in medical imaging, BREIT addresses the lack of standardized datasets and pipelines for deep-learning-based 3D EIT stroke reconstruction, though the improvement is incremental.
BREIT provides a modular framework for 3D multi-frequency EIT stroke reconstruction, including a neuroimaging-to-EIT pipeline, forward solver, and D-bar implementation. The proposed dFNO-bar method achieves higher brain SSIM with comparable correlation coefficient against baselines on synthetic data.
Multi-Frequency Electrical Impedance Tomography (MF-EIT) is a non-invasive, low-cost modality that reconstructs electrical property distributions from boundary voltages. For stroke imaging, progress in 3D deep-learning reconstruction is limited by the lack of large-scale datasets with paired ground-truth (GT) volumes and by non-standardized pipelines for data generation, simulation, and evaluation. We introduce BREIT, a modular framework for 3D MF-EIT stroke reconstruction providing: (i) a neuroimaging-to-EIT pipeline that converts CT/MRI into frequency-dependent GT admittivity volumes; (ii) a self-contained Python 3D Complete Electrode Model (CEM) forward solver for simulating MF-EIT voltages; and (iii) a 3D D-bar implementation supporting non-uniform electrode layouts. Building on BREIT, we propose dFNO-bar, which integrates Fourier Neural Operators into D-bar by learning a mapping from scattering data $t(ξ)$ to conductivity $σ(x){=}\Re\{γ\}$. We evaluate dFNO-bar against D-bar, Deep D-bar, and Gauss--Newton reconstructions on UCLH-matched synthetic data, and observe higher brain SSIM with comparable CC across noise settings. Code and data are publicly available at: https://github.com/djahiddj13/BREIT