用图神经网络提升多频电导率成像重建精度
Deep Unfolding Network for Nonlinear Multi-Frequency Electrical Impedance Tomography
- 将非线性迭代算法展开为深度网络，每层对应一次迭代
- 利用GNN捕捉多频间相关性，重建结果更准确
- 适合需要高精度生物组织成像的研究者
多频电导率断层成像（mfEIT）是一种有前景的生物医学成像技术，可估计组织在不同频率下的电导率。本文提出一种新型变分网络，融合经典迭代重建的可解释性与深度学习的强大能力。该方法将图神经网络（GNN）嵌入到非线性近似正则化高斯-牛顿（PRGN）框架中，通过展开PRGN算法，使每次迭代对应网络的一层，从而结合物理模型的非线性拟合优势与GNN对跨频率相关性的建模能力。特别地，GNN架构保留了求解非线性前向模型时使用的不规则三角网格结构，实现了对重叠组织浓度分布的精确重建。
原文摘要 · Abstract (English)
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 unrolling 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.
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