Assessing the impact of a structural prior mask on EIT images with different thorax excursion models

Abstract

Electrical Impedance Tomography (EIT) has shown promising results as a low-cost imaging method for visualizing ventilation distribution within the lungs. However, clinical interpretation of EIT images is often hindered by blurred anatomical alignment and reconstruction artifacts. Integrating structural priors into the EIT reconstruction process has the potential to enhance the interpretability of the EIT images. Thus, a patient-specific structural prior mask is introduced in this contribution, which restricts the reconstruction of conductivity changes within the lung regions.We conducted numerical simulations on four finite element models representing four different thorax excursions to investigate the impact of the structural prior mask on EIT images. Simulations were performed under four different ventilation statuses. EIT images were reconstructed using the Gauss-Newton and discrete cosine transform-based EIT algorithms.We conducted a quantitative analysis using figures of merit to evaluate the images of the two reconstruction algorithms. The results show the structural prior mask preserves the morphological structures of the lungs and limits reconstruction artifacts.

Keywords

• electrical impedance tomography
• structural prior
• thorax shape
• algorithm