StructEIT: Realistic 3D EIT model generation from CT scans for deep learning applications.
Authors
Affiliations (5)
Affiliations (5)
- Shanghai Jiao Tong University, 800 Dongchuan RD. Minhang District, Shanghai, China, Shanghai, 200240, China.
- Shanghai Jiao Tong University, No.800, Dongchuan Road, Minhang District, Shanghai, China, Shanghai, 200240, China.
- School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, 800 Dongchuan Road Shanghai, Shanghai, 200240, China.
- Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, 160 Pujian Road, Shanghai, Shanghai, 200127, China.
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
Abstract
Artificial intelligence (AI) has significantly improved image reconstruction quality across various medical imaging modalities. However, its application in electrical impedance tomography (EIT) reconstruction remains limited, mainly due to the absence of comprehensive in vivo datasets that incorporate realistic anatomical geometries and conductivity distributions. This limitation constrains the development of supervised and data-driven reconstruction methods. To address this bottleneck, we developed StructEIT, an integrated EIT modeling framework for generating anatomically and biophysically realistic EIT simulation models. The framework incorporates three key components: (1) a structure extraction module, which automatically processes human CT scans to extract body contours and organ boundaries, thereby providing high-fidelity spatial geometry for 3D finite element modeling; (2) a surface electrode attachment module, which enables flexible and accurate placement of electrodes on irregular body surfaces, supporting diverse configurations and ensuring precise definition of the electrode-tissue interface; and (3) a tissue property assignment module, which establishes frequency-dependent conductivity models for multiple organs, enabling physiologically realistic conductivity values across tissues. Main results and Significance. By bridging the gap between CT imaging and EIT, StructEIT facilitates flexible, realistic, and scalable generation of high-resolution EIT datasets. Using this this framework, we constructed Chest-EIT, a thoracic EIT simulation dataset comprising over 1,400 publicly available CT cases, with multiple electrode configurations provided for each case.