Monotonicity-based Electrical Impedance Tomography for Lung Imaging

Abstract

This paper presents a monotonicity-based spatiotemporal conductivity imaging method for continuous regional lung monitoring using electrical impedance tomography (EIT). The EIT data (i.e., the boundary current-voltage data) can be decomposed into pulmonary, cardiac and other parts using their different periodic natures. The time-differential current-voltage operator corresponding to the lung ventilation can be viewed as either semi-positive or semi-negative definite owing to monotonic conductivity changes within the lung regions. We used this monotonicity constraints to improve the quality of lung EIT imaging. We tested the proposed methods in numerical simulations, phantom experiments and human experiments.

27 pages, 17 figures

References in corpus (6)

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• On localizing and concentrating electromagnetic fields
• Dimension bounds in monotonicity methods for the Helmholtz equation
• Uniqueness, stability and global convergence for a discrete inverse elliptic Robin transmission problem
• Simultaneous recovery of piecewise analytic coefficients in a semilinear elliptic equation
• Adaptive Reconstruction for Electrical Impedance Tomography with a Piecewise Constant Conductivity
• The Monotonicity Principle for Magnetic Induction Tomography