Experimental/Clinical Evaluation Of Eit Image Reconstruction With L(1) Data And Image Norms

Electrical impedance tomography (EIT) image reconstruction is ill-posed, and the spatial resolution of reconstructed images is low due to the diffuse propagation of current and limited number of independent measurements. Generally, image reconstruction is formulated using a regularized scheme in which 2 norms are preferred for both the data misfit and image prior terms due to computational convenience which result in smooth solutions. However, recent work on a Primal Dual-Interior Point Method (PDIPM) framework showed its effectiveness in dealing with the minimization problem. l(1) norms on data and regularization terms in EIT image reconstruction address both problems of reconstruction with sharp edges and dealing with measurement errors. We aim for a clinical and experimental evaluation of the PDIPM method by selecting scenarios (human lung and dog breathing) with known electrode errors, which require a rigorous regularization and cause the failure of reconstructions with 2 norm. Results demonstrate the applicability of PDIPM algorithms, especially l(1) data and regularization norms for clinical applications of EIT showing that l(1) solution is not only more robust to measurement errors in clinical setting, but also provides high contrast resolution on organ boundaries.