Medical engineering & physics · 2021
Diagnosing and monitoring pleural effusion using parametric electrical impedance tomography - a computational 3D model and preliminary experimental results.
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Omer Noam, Abboud Shimon, Arad Marina
Аннотация
PURPOSE: Diagnosing and monitoring pleural effusion (PE) is challenging due unsuitability of existing modalities. In the present study, a novel parametric electrical impedance tomography (pEIT) technique, tailored to a clinically feasible system to diagnose PE is presented.
METHODS: An electrical impedance tomography (EIT) numeric solver was applied to a 3D realistic normal model and five PE models to simulate sets of surface measurements. Simulations were triggered by a series of eight independent projections using five electrodes positioned around the thorax. The relative changes in the potential between the PE models and the normal model were assessed and the error in the estimated PE volume was examined at varying signal to noise ratio (SNR) levels. For experimental feasibility, measurements were performed in four healthy subjects and were correlated with the potentials that were calculated from the normal model.
RESULTS: Relative potential changes were notable (reached until ~55%) and increased with the increasing PE volumes. Maximal error of ± 20 [mL] was obtained for SNR levels >50 [dB]. The feasibility real measurements in healthy subjects showed a strong linear correlation (R2 > 0.85) and a successful diagnosis for all subjects.
CONCLUSION: The proposed technique can estimate PE volumes from a redundant set of measurements in a realistic 3D human model and may be utilized for monitoring PE patients.
METHODS: An electrical impedance tomography (EIT) numeric solver was applied to a 3D realistic normal model and five PE models to simulate sets of surface measurements. Simulations were triggered by a series of eight independent projections using five electrodes positioned around the thorax. The relative changes in the potential between the PE models and the normal model were assessed and the error in the estimated PE volume was examined at varying signal to noise ratio (SNR) levels. For experimental feasibility, measurements were performed in four healthy subjects and were correlated with the potentials that were calculated from the normal model.
RESULTS: Relative potential changes were notable (reached until ~55%) and increased with the increasing PE volumes. Maximal error of ± 20 [mL] was obtained for SNR levels >50 [dB]. The feasibility real measurements in healthy subjects showed a strong linear correlation (R2 > 0.85) and a successful diagnosis for all subjects.
CONCLUSION: The proposed technique can estimate PE volumes from a redundant set of measurements in a realistic 3D human model and may be utilized for monitoring PE patients.