  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.
