A Method to Suppress Contact Impedance Noise under a Framework of CT Image-Guided EIT for Pulmonary Ventilation Monitoring

The performance of an Electrical Impedance Tomography (EIT) system in prolonged pulmonary ventilation monitoring is critically influenced by contact impedance noise. This paper proposes a method for suppressing contact impedance noise within a framework of CT image-guided EIT, aiming to enhance the imaging quality of pulmonary ventilation. First, a full-link noise model is introduced, linking EIT system noises to image artifacts. The distribution characteristics of artifacts caused by different types of noise were then analyzed, revealing that contact impedance noise primarily induces edge-concentrated artifacts. Second, a CT image-guided EIT imaging scheme based on cosine similarity is developed to improve image quality and separate the edge artifacts. The inverse problem is solved using the Gauss-Newton iteration, which can be computationally reduced to the conjugate gradient method for real-time reconstruction. Finally, stemming from the full link noise model and CT-EIT scheme, an adaptive method is proposed to estimate and suppress the contact impedance noise in real-time from the artifact distribution in an EIT image. A series of simulations and experimental tests are carried out to validate the performance of the proposed method. It is indicated that the severe artifacts caused by the contact impedance noise can be significantly suppressed, with the accuracy of lung shape improved considerably. Overall, the proposed method can serve as an adaptive on-line approach to enhance and maintain the EIT imaging quality and stability of pulmonary ventilation monitoring, especially when long-term monitoring is required where the contact noise is inevitable due to unconstrained patient movement.