Gastric Electrical Impedance Tomography (gEIT) Based on a 3D Jacobian Matrix and Dual-Step Fuzzy Clustering Post-Processing

We propose a gastric electrical impedance tomography (
${g}$
EIT) scheme that uses a 3D full Jacobian matrix
$^\ast \text{J}$
and dual-step fuzzy clustering post-processing to automatically extract the gastric content volume
${V}$
.
${g}$
EIT has two stages: (i) computation of the full 3D Jacobian matrix
$^\ast \text{J}$
, including all combinations of the current injection pattern
${I}$
and the impedance measurement
${Z}$
, in order to reconstruct the volumetric abdomen conductivity
$\sigma $
; and (ii) clustering of the gastric composition
$^{k} \sigma $
(gastric wall
${k}={1}$
, gastric content
${k}={2}$
) using dual-step fuzzy clustering post-processing, in order to automatically extract the volumetric gastric conductivity
$^{k} \sigma $
. Our
${g}$
EIT scheme is qualitatively evaluated using realistic abdomen phantoms representing three gastric accommodation phases, based on three different values for the gastric content volume filled with liquid meal: (A)
${V}_{A} = {100}$
mL; (B)
${V}_{B} = {300}$
mL; and (C)
${V}_{C} = {600}$
mL, for two abdomen shapes (a normal abdomen
${S}$
and a fatty abdomen
${L}$
) through both simulation and experimental studies. The results show that
${g}$
EIT automatically estimates
${V}$
with a total mean volume error
$\langle \widetilde {V}_{e}\rangle $
that is 65.14% lower than a standard adjacent method. At the same time,
${g}$
EIT detects the gastric conductivity
$\sigma $
more effectively, with a total mean conductivity error
$\langle \widetilde {\textit {CE}} \rangle $
and total mean root mean square error
$\langle \widetilde {\textit {rmse}}\rangle ~33.68$
% and 25.53% lower than a standard adjacent method, respectively.