Experimental/clinical evaluation of EIT image reconstruction with ℓ₁ 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 ℓ₂ 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. ℓ₁ 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 ℓ₂ norm. Results demonstrate the applicability of PDIPM algorithms, especially ℓ₁ data and regularization norms for clinical applications of EIT showing that ℓ₁ solution is not only more robust to measurement errors in clinical setting, but also provides high contrast resolution on organ boundaries.