Andy Adler et al 2007 Physiol. Meas. 28 S1 doi:10.1088/0967-3334/28/7/S01
Andy Adler1, Tao Dai1 and William R B Lionheart2
Show affiliationsElectrical 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
and measurement vector
, which concatenate the values from the d previous and future frames. Image reconstruction is then based on an augmented regularization matrix
, which accounts for a model of both the spatial and temporal correlations between image elements. Results are compared for reconstruction algorithms based on independent frames, Kalman filters and the proposed approach. For low values of the regularization hyperparameter, the proposed approach performs similarly to independent frames, but for higher hyperparameter values, it uses adjacent frame data to reduce reconstructed image noise.
Issue 7 (July 2007)
Received 13 December 2006, accepted for publication 3 April 2007
Published 26 June 2007
Andy Adler et al 2007 Physiol. Meas. 28 S1
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