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Summary: Temporal Image Reconstruction in Electrical
Impedance Tomography
Andy Adler1
, Tao Dai1
, William R.B. Lionheart2
1
Systems and Computer Engineering, Carleton University, Ottawa, Canada
2
School of Mathematics, University of Manchester, UK
E-mail: adler@sce.carleton.ca
Abstract. Electrical Impedance Tomography (EIT) calculates images of the body
from body impedance measurements. While the spatial resolution of these images
is relatively low, the temporal resolution of EIT data can be high. Most EIT
reconstruction algorithms solve each data frame independently, although Kalman filter
algorithms track the image changes across frames. This paper proposes a new approach
which directly accounts for correlations between images in successive data frames.
Image reconstruction is posed in terms of an augmented image ~x and measurement
vector ~y, which concatenate the values from the d previous and future frames. Image
reconstruction is then based on an augmented regularization matrix ~R, which accounts
for a model of both the spatial and temporal correlations between image elements.
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