提出BREIT框架，实现脑卒中3D电阻抗成像高精度重建。
BREIT: A Framework for Brain Stroke Reconstruction using Multi-Frequency 3D EIT
- 构建从CT/MRI到电导率体积的转换管道，生成多频域真实数据
- 在合成数据上，新方法比传统算法提升脑部结构相似性（SSIM）
- 适合医学成像、深度学习与电学逆问题研究者使用
多频段电阻抗断层成像（MF-EIT）是一种无创、低成本的成像方式，通过边界电压重建电学属性分布。针对脑卒中成像，3D深度学习重建进展受限于缺乏大规模配对真实数据集，以及数据生成、仿真和评估流程不统一。本文提出BREIT框架，包含：(i) 从神经影像转为频率依赖的真实电导率体积的转化管道；(ii) 自包含的Python 3D完整电极模型（CEM）正向求解器，用于模拟多频段EIT电压；(iii) 支持非均匀电极布局的3D D-bar实现。基于BREIT，我们提出dFNO-bar，将傅里叶神经算子融入D-bar，学习散射数据 $t(ξ)$ 到电导率 $σ(x){=} ext{Re} {γ}$ 的映射。在与UCLH匹配的合成数据上，评估结果表明，dFNO-bar在不同噪声水平下，脑部结构相似性（SSIM）更高，相关系数（CC）相当。
原文摘要 · Abstract (English)
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.
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