Influence of the Patient-Specific Structural Prior Mask on Image Reconstruction Using the Discrete Cosine Transform-Based EIT Algorithm

Electrical impedance tomography (EIT) is an imaging technology but suffers greatly from the ill-posed inverse problem when reconstructing an image, which is mainly caused by the high degrees of freedom and the relatively large measurement noise. The use of discrete cosine transform (DCT) to cluster finite elements has been proposed to reduce the degrees of freedom in inverse computations. However, blurred anatomical alignment and artifacts still present challenges to the interpretation of EIT images. Incorporating prior information into the reconstruction process has been reported to enhance the quality of EIT images. In this contribution, we propose the use of a patient-specific structural prior mask for the DCT-based EIT algorithm. We evaluate the influence of this mask on simulation models with varying ventilation statuses. Our results demonstrate that the structural prior mask preserves the morphological structures of the lungs and avoids blurring of the solution, thereby facilitating EIT image interpretation for clinicians.