Extraction of Pendelluft Features from Electrical Impedance Tomography Images

Abstract

Pendelluft corresponds to the air flow from a nondependent lung region to a dependent one. Its occurrence during spontaneous breathing in mechanical ventilation is hard to detect in clinical routines and can lead to injuries. The aim of this study was to develop an automated methodology to diagnose Pendelluft from 2D + time Electrical Impedance Tomography sequences. Our images were acquired using an electrical impedance tomograph (1800 Enlight, Timpel, São Paulo, Brasil) from a normal pig (N) and a pig with the condition (P). Image analysis was performed in Matlab, divided into the following stages: (i) creation of motion vector field (MVF) using Horn and Schunck optical flow; (ii) decomposition of the MVF into a curl-free and divergence-free scalar potentials, D and R respectively, using the Discrete Helmholtz-Hodge Decomposition; (iii) the location of extrema in D and R were tracked over the image sequences N and P, and frequency maps created for the right and left lungs. The degree of similarity of these maps was represented by a parameter $\phi$. The values of $\phi$ for P were zero for both lungs in the R and D analysis. In the D field analysis, pig N showed $\phi$ values of 6031 and 5407 for respective right and left lungs. The same analysis in the R field gave us a value of 4836 and 4538 for right and left lungs. These results show that our methodology is a possible candidate for automatic detection of Pendelluft, but studies from a large number of human subjects would be needed.