Influence of reconstruction settings in electrical impedance tomography on figures of merit and physiological parameters

Abstract

Objective: Electrical impedance tomography (EIT) is a non-invasive and relatively cheap imaging technique allowing continuous monitoring of lung function at the bedside. However, image reconstruction and processing are not yet standardized for clinical use, limiting comparability and reproducibility between studies. In addition, optimal reconstruction settings still have to be identified for different clinical applications. In this work (i) a systematic way to select ‘good’ EIT algorithm parameters is developed and (ii) an evaluation of these parameters in terms of correct functional imaging and consistency is performed. Approach: First, 19 200 reconstruction models are generated by full factorial design of experiment in 5D space. Then, in order to quantify the quality of reconstruction, known conductivity changes are introduced and figures of merit (FoM) are calculated from the response image. These measures are further used to select a subset of reconstruction models, matching certain FoM thresholds, and are then used for in vivo evaluation. For this purpose, EIT images of one piglet are reconstructed to assess changes in tidal impedance and end-expiratory lung impedance, at positive end expiratory pressure of 0 and 15 cmH2O. From ground truth spirometry measurements, physiological criteria are formulated and the subset of models is further reduced. Finally, the remaining reconstruction models are evaluated on physiological data gathered from published data in the literature to assess the generalization possibilities. Main results: Parametrization of EIT image reconstruction has a strong influence on the resulting FoM and the derived physiological parameter. While numerous reconstruction models showed reasonable values for a single parameter, in total only 12 matched all simulation and physiological criteria. After validation on further physiological data, only a single reconstruction model remained with a noise figure of 0.3, target size of 0.08, weight radius of 0.3, normalized voltage and strong weighting of lung and heart regions. Furthermore, the relationship between the reconstruction settings and some FoM could be partly explained by using a linear statistical model. Significance: The quest for standard reconstruction settings is highly relevant for future clinical applications. Simulation measures might help to assess the quality of the reconstruction models, but further evaluation of more data and different experimental settings is required.