StructEIT: realistic 3D EIT model generation from CT scans for deep learning applications

Jiang, Zeyi

doi:10.1088/1361-6579/ae5587

2026-04-30

PRE

DOI：10.1088/1361-6579/ae5587

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摘要

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目的。人工智能已显著提高各种医学成像模态的图像重建质量。然而，其在电阻抗断层成像（EIT）重建中的应用仍然有限，这主要归因于缺乏包含真实解剖几何形状和电导率分布的综合活体数据集。这种限制约束了监督和数据驱动的重建方法的发展。方法。为解决这一瓶颈，我们开发了StructEIT，一个用于生成解剖学和生物物理学上真实的EIT仿真模型的集成EIT建模框架。该框架包含三个关键组件：（1）结构提取模块，可自动处理人体CT扫描以提取身体轮廓和器官边界，从而为三维有限元建模提供高保真空间几何；（2）表面电极固定模块，可在不规则体表上实现灵活且精确的电极放置，支持多样化配置并确保电极-组织界面的精确定义；（3）组织属性分配模块，为多个器官建立频率相关电导率模型，使组织间电导率值符合生理学现实。主要结果和意义。通过弥合CT成像与EIT之间的差距，StructEIT促进了灵活、真实且可扩展的高分辨率EIT数据集的生成。使用此框架，我们构建了Chest-EIT，一个包含1400多个公开可用CT病例的胸部EIT仿真数据集，每个病例均提供多种电极配置。

Keyword:

electrical impedance tomography

finite element modeling

anatomical segmentation

simulation dataset

期刊

Physiological Measurement

IF:

2.7

论文数:

103

被引数:

7.3K

机构

shanghai jiao tong university

学者数:

15.7W

论文数: 11.7W

被引数: 159

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