Large-Scale Non-Linear 3D Reconstruction Algorithms for Electrical Impedance Tomography of the Human Head

Abstract

Non-linear image reconstruction methods are desirable for applications in electrical impedance tomography (EIT) such as brain or breast imaging where the assumptions of linearity are violated. We present a novel non-linear Newton-Krylov method for solving large-scale EIT inverse problems, which has the potential advantages of improved robustness and computational efficiency over previous methods. This combines Krylov-subspace efficiency in the production of an implicit Hessian inverse together with the Newton-type search direction effectiveness. The computational cost was assessed by comparing the objective function value and image error norm with respect to run-time, iteration count and memory consumption with six other non-linear methods, including Damped Newton-Gauss, Levenberg-Marquardt, Variable Metric and non-linear Conjugated Gradients, using realistic layered head models with meshes of 4, 12 and 31K elements. For the small-scale model, Newton-type methods slightly outperformed the Krylov-Newton approach, while the other large-scale methods performed poorly. For the larger two models, the Newton-Krylov approach converged much more rapidly than the Krylov-subspace and quasi-Newton methods; Newton-type methods failed to converge in the time available. This approach opens a new frontier for non-linear EIT image reconstruction, as it allows production of accurate solutions of large-scale realistic models using modest computational resources.