  Electrical impedance tomography (EIT) provides functional images of an
electrical conductivity distribution inside the human body. Since the 1980s,
many potential clinical applications have arisen using inexpensive portable EIT
devices. EIT acquires multiple trans-impedance measurements across the body
from an array of surface electrodes around a chosen imaging slice. The
conductivity image reconstruction from the measured data is a fundamentally
ill-posed inverse problem notoriously vulnerable to measurement noise and
artifacts. Most available methods invert the ill-conditioned sensitivity or
Jacobian matrix using a regularized least-squares data-fitting technique. Their
performances rely on the regularization parameter, which controls the trade-off
between fidelity and robustness. For clinical applications of EIT, it would be
desirable to develop a method achieving consistent performance over various
uncertain data, regardless of the choice of the regularization parameter. Based
on the analysis of the structure of the Jacobian matrix, we propose a
fidelity-embedded regularization (FER) method and a motion artifact removal
filter. Incorporating the Jacobian matrix in the regularization process, the
new FER method with the motion artifact removal filter offers stable
reconstructions of high-fidelity images from noisy data by taking a very large
regularization parameter value. The proposed method showed practical merits in
experimental studies of chest EIT imaging.
