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We show that electrical impedance tomography (EIT) image reconstruction algorithms with regularization based on the total variation (TV) functional are suitable for in vivo imaging of physiological data. This reconstruction approach helps to preserve discontinuities in reconstructed profiles, such as step changes in electrical properties at interorgan boundaries, which are typically smoothed by traditional reconstruction algorithms. The use of the TV functional for regularization leads to the minimization of a nondifferentiable objective function in the inverse formulation. This cannot be efficiently solved with traditional optimization techniques such as the Newton method. We explore two implementations methods for regularization with the TV functional: the lagged diffusivity method and the primal dual-interior point method (PD-IPM). First we clarify the implementation details of these algorithms for EIT reconstruction. Next, we analyze the performance of these algorithms on noisy simulated data. Finally, we show reconstructed FIT images of in vivo data for ventilation and gastric emptying studies. In comparison to traditional quadratic regularization, TV regularization shows improved ability to reconstruct sharp contrasts. [ABSTRACT FROM AUTHOR]

Provides information on a study which reviewed some numerical techniques based on the linear Krylov subspace iteration that can be used for the efficient calculation of the forward and the inverse electrical impedance tomography (EIT) problems in human brain activities. Use of the pre-conditioned conjugate gradients algorithm; Basis for the imaging capabilities of EIT; Requirement for the forward problem.

Discusses a method for constructing Gaussian anisotropic filters for a piece-wise constant two-dimensional finite-element method forward model for electrical impedance tomography (EIT). Use of the Tikhonov regularization functional system; Formulation of the inverse problem; Discussion on anisotropic Gaussian smoothing.