Electrical impedance tomography (EIT) produces an image of internal conductivity distributions in a body from current injection and electrical measurements at surface electrodes. Typically, image reconstruction is formulated using regularized schemes in which ℓ2-norms are used for both data misfit and image prior terms. Such a formulation is computationally convenient, but favours smooth conductivity solutions and is sensitive to outliers. Recent studies highlighted the potential of ℓ1-norm and provided the mathematical basis to improve image quality and robustness of the images to data outliers. In this paper, we (i) extended a primal-dual interior point method (PDIPM) algorithm to 2.5D EIT image reconstruction to solve ℓ1 and mixed ℓ1/ ℓ2 formulations efficiently, (ii) evaluated the formulation on clinical and experimental data, and (iii) developed a practical strategy to select hyperparameters using the L-curve which requires minimum user-dependence. The PDIPM algorithm was evaluated using clinical and experimental scenarios on human lung and dog breathing with known electrode errors, which requires a rigorous regularization and causes the failure of reconstruction with an ℓ2-norm solution. The results showed that an ℓ1 solution is not only more robust to unavoidable measurement errors in a clinical setting, but it also provides high contrast resolution on organ boundaries.

Additional Metadata
Keywords electrical impedance tomography, L1 norm, L2 norm, Primal Dual Interior Point Method, regularization
Persistent URL dx.doi.org/10.1088/0967-3334/34/9/1027
Journal Physiological Measurement
Mamatjan, Y. (Yasin), Borsic, A. (Andrea), Gürsoy, D. (Doga), & Adler, A. (2013). An experimental clinical evaluation of EIT imaging with ℓ1 data and image norms. Physiological Measurement, 34(9), 1027–1039. doi:10.1088/0967-3334/34/9/1027