Conditional Diffusion Model for Electrical Impedance Tomography

• URL: http://arxiv.org/abs/2501.05769v1
• Date: Fri, 10 Jan 2025 07:58:38 GMT
• Title: Conditional Diffusion Model for Electrical Impedance Tomography
• Authors: Duanpeng Shi, Wendong Zheng, Di Guo, Huaping Liu,
• Abstract summary: Electrical impedance tomography (EIT) is a non-invasive imaging technique, which has been widely used in the fields of industrial inspection, medical monitoring and tactile sensing.<n>Due to the inherent non-linearity and ill-conditioned nature of the EIT inverse problem, the reconstructed image is highly sensitive to the measured data, and random noise artifacts often appear in the reconstructed image.<n>A conditional diffusion model with voltage consistency (CDMVC) is proposed in this study to address this issue.
• Score: 17.831065873724153
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrical impedance tomography (EIT) is a non-invasive imaging technique, which has been widely used in the fields of industrial inspection, medical monitoring and tactile sensing. However, due to the inherent non-linearity and ill-conditioned nature of the EIT inverse problem, the reconstructed image is highly sensitive to the measured data, and random noise artifacts often appear in the reconstructed image, which greatly limits the application of EIT. To address this issue, a conditional diffusion model with voltage consistency (CDMVC) is proposed in this study. The method consists of a pre-imaging module, a conditional diffusion model for reconstruction, a forward voltage constraint network and a scheme of voltage consistency constraint during sampling process. The pre-imaging module is employed to generate the initial reconstruction. This serves as a condition for training the conditional diffusion model. Finally, based on the forward voltage constraint network, a voltage consistency constraint is implemented in the sampling phase to incorporate forward information of EIT, thereby enhancing imaging quality. A more complete dataset, including both common and complex concave shapes, is generated. The proposed method is validated using both simulation and physical experiments. Experimental results demonstrate that our method can significantly improves the quality of reconstructed images. In addition, experimental results also demonstrate that our method has good robustness and generalization performance.

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