IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. X, NO. X, MONTH 20121
Impact of model shape mismatch on reconstruction
quality in Electrical Impedance Tomography
Bartłomiej Grychtol, William RB Lionheart, Marc Bodenstein, Gerhard K Wolf and Andy Adler
Abstract—Electrical Impedance Tomography (EIT) is a low-
cost, non-invasive and radiation free medical imaging modality
for monitoring ventilation distribution in the lung. Although
such information could be invaluable in preventing ventilator-
induced lung injury in mechanically ventilated patients, clinical
application of EIT is hindered by difficulties in interpreting the
resulting images. One source of this difficulty is the frequent use
of simple shapes which do not correspond to the anatomy to
reconstruct EIT images. The mismatch between the true body
shape and the one used for reconstruction is known to introduce
errors, which to date have not been properly characterized. In the
present study we therefore seek to 1) characterize and quantify
the errors resulting from a reconstruction shape mismatch for
a number of popular EIT reconstruction algorithms and 2)
develop recommendations on the tolerated amount of mismatch
for each algorithm. Using real and simulated data, we analyze the
performance of 4 EIT reconstruction algorithms under different
degrees of shape mismatch. Results suggest that while slight shape
mismatch is well tolerated by all algorithms, using a circular
shape severely degrades their performance.
Index Terms—EIT, model, shape, mechanical ventilation, ALI,
distribution in the lung. In thoracic EIT, imperceptible current
injection and voltage measurement through surface electrodes
around the thorax are used to reconstruct a conductivity map
across a transverse slice of the body. EIT is low-cost, non-
invasive, radiation free and available at the bedside. One of
the most promising applications of EIT is for monitoring
and/or guiding mechanical ventilation therapy. The ability
of EIT to measure regional distribution of ventilation has
been validated against single photon emission computed to-
mography (SPECT) , X-ray computed tomography (CT)
,  and positron emission tomography (PET) . No
LECTRICAL Impedance Tomography (EIT) is a promis-
ing medical imaging modality for monitoring ventilation
B Grychtol is with the German Cancer Research Centre (DKFZ), De-
partment of Medical Physics in Radiology, 69120 Heidelberg, Germany
WRB Lionheart is with School of Mathematics, University of Manchester,
Manchester M13 9PL, England
M Bodenstein is with Department of Anesthesiology, Johannes Gutenberg-
University Mainz, Mainz, Germany
GK Wolf is with the Division of Critical Care Medicine, Department of
Anesthesiology, Children’s Hospital Boston, Harvard Medical School, Boston,
MA 02115, USA
A Adler is with Systems and Computer Engineering, Carleton University,
Ottawa, ON K1S 5B6, Canada
Manuscript received March 22, 2012; revised May 14, 2012
Copyright c ?2012 IEEE. Personal use of this material is permitted. How-
ever, permission to use this material for any other purposes must be obtained
from the IEEE by sending a request to email@example.com.
other currently available technology can provide real-time long
term monitoring of the regional functional state of the lungs.
Although such information could be invaluable in preventing
ventilator-induced lung injury (VILI), clinical application of
EIT is hindered by difficulties in interpreting the resulting
Such difficulties are often a result of errors in the for-
ward modeling of the electrical fields, a necessary step in
reconstructing the conductivity distribution. In particular, no
two-dimensional model can fit EIT data obtained from a
three-dimensional domain (body)  and, even when a three-
dimensional model of a domain is used, it is generally im-
possible to accurately fit data from an isotropic conductivity
distribution if the boundary shape is wrong .
Because in clinical practice the boundary shape is generally
unknown and changes with breathing and posture, the problem
is often reduced to reconstructing the changes rather than ab-
solute conductivity, which is less sensitive to shape mismatch
and easier to solve. A circular shape has traditionally been used
to represent a cross-section of the subject’s body . This lack
of correspondence to the anatomy imposes several limitations
on the analysis of EIT images. Because expected organ shape
and position on circular images is unknown, it is difficult
to distinguish some artifacts from correct images. Images of
different patients cannot be directly compared. Moreover, the
mismatch between the true body shape and the shape used
for reconstruction is known to produce image errors , ,
which to date have not been properly characterized.
In a preliminary study of one reconstruction algorithm ,
we showed that using the correct body shape obtained from
a CT scan produces reconstructions qualitatively superior to
those produced with a circular shape. However, for practical
reasons, EIT reconstruction cannot depend on the availability
of a CT scan of each individual subject. Patient shape could
instead be obtained by means of, for example, wearable sen-
sors or through optical 3D surface reconstruction (from images
obtained with a multi-camera system). However, we believe
that developing a set of pre-defined shapes to choose from
for each patient based on easy to measure parameters (weight,
height, etc.) is the most practical and least expensive approach.
In order to develop such a set, a deeper understanding of the
errors and tolerances of different EIT algorithms with respect
to shape mismatch is required.
In the present study we therefore seek to 1) characterize and
quantify the errors resulting from reconstruction shape mis-
match for a number of popular EIT reconstruction algorithms
and 2) develop recommendations on the tolerated amount of
mismatch for each algorithm.
8IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. X, NO. X, MONTH 2012
 P. W. Kunst, A. Vonk Noordegraaf, O. S. Hoekstra, P. E. Postmus,
and P. M. de Vries, “Ventilation and perfusion imaging by electrical
impedance tomography: a comparison with radionuclide scanning,”
Physiological measurement, vol. 19, no. 4, pp. 481–90, Nov. 1998.
 I. Frerichs, J. Hinz, P. Herrmann, G. Weisser, G. Hahn, T. Dudykevych,
M. Quintel, and G. Hellige, “Detection of local lung air content by
electrical impedance tomography compared with electron beam CT,”
Journal of applied physiology, vol. 93, no. 2, pp. 660–6, Aug. 2002.
 J. A. Victorino, J. a. B. Borges, V. N. Okamoto, G. F. J. Matos,
M. R. Tucci, M. P. R. Caramez, H. Tanaka, F. S. Sipmann, D. C. B.
Santos, C. S. V. Barbas, C. R. R. Carvalho, and M. B. P. Amato,
“Imbalances in regional lung ventilation: a validation study on electrical
impedance tomography,” American journal of respiratory and critical
care medicine, vol. 169, no. 7, pp. 791–800, Apr. 2004.
 J. C. Richard, C. Pouzot, A. Gros, C. Tourevieille, D. Lebars, F. Lavenne,
I. Frerichs, and C. Gu´ erin, “Electrical impedance tomography compared
to positron emission tomography for the measurement of regional lung
ventilation: an experimental study,” Critical care (London, England),
vol. 13, no. 3, p. R82, Jan. 2009.
 W. R. B. Lionheart, “Uniqueness, shape, and dimension in EIT,” Annals
of the New York Academy of Sciences, vol. 873, no. 1 ELECTRICAL
BI, pp. 466–471, Apr. 1999.
 ——, “Boundary shape and electrical impedance tomography,” Inverse
Problems, vol. 14, no. 1, pp. 139–147, Feb. 1998.
 D. C. Barber and B. H. Brown, “Errors in reconstruction of resistivity
images using a linear reconstruction technique,” Clinical Physics and
Physiological Measurement, vol. 9, no. 4A, pp. 101–104, Nov. 1988.
 A. Adler, R. Guardo, and Y. Berthiaume, “Impedance imaging of lung
ventilation: do we need to account for chest expansion?” IEEE Trans.
Biomed. Eng., vol. 43, no. 4, pp. 414–20, Apr. 1996.
 B. Grychtol, W. R. B. Lionheart, G. K. Wolf, M. Bodenstein, and
A. Adler, “The importance of shape: thorax models for GREIT,” in 12th
International Conference on Electrical Impedance Tomography, Bath,
 A. Adler, J. H. Arnold, R. Bayford, A. Borsic, B. Brown, P. Dixon,
T. J. C. Faes, I. Frerichs, H. Gagnon, Y. G¨ arber, B. Grychtol, G. Hahn,
W. R. B. Lionheart, A. Malik, R. P. Patterson, J. Stocks, A. Tizzard,
N. Weiler, and G. K. Wolf, “GREIT: a unified approach to 2D linear
EIT reconstruction of lung images,” Physiological measurement, vol. 30,
no. 6, pp. S35–55, Jun. 2009.
 A. Adler and W. R. B. Lionheart, “Uses and abuses of EIDORS: an
extensible software base for EIT,” Physiological measurement, vol. 27,
no. 5, pp. S25–42, May 2006.
 J. Sch¨ oberl, “NETGEN an advancing front 2D/3D-mesh generator based
on abstract rules,” Computing and Visualization in Science, vol. 1, no. 1,
pp. 41–52, Jul. 1997.
 T. Murai and Y. Kagawa, “Electrical impedance computed tomography
based on a finite element model,” IEEE Trans. Biomed. Eng., vol. BME-
32, no. 3, pp. 177–184, Mar. 1985.
 R. Bayford, P. Kantartzis, a. Tizzard, R. Yerworth, P. Liatsis, and
A. Demosthenous, “Development of a neonate lung reconstruction algo-
rithm using a wavelet amg and estimated boundary form,” Physiological
measurement, vol. 29, no. 6, pp. S125–38, Jun. 2008.
 M. Cheney, D. Isaacson, J. C. Newell, S. Simske, and J. Goble,
“NOSER: An algorithm for solving the inverse conductivity problem,”
International Journal of Imaging Systems and Technology, vol. 2, no. 2,
pp. 66–75, 1990.
 M. Vauhkonen, D. Vadasz, P. Karjalainen, E. Somersalo, and J. Kaipio,
“Tikhonov regularization and prior information in electrical impedance
tomography,” IEEE Trans. Med. Imag., vol. 17, no. 2, pp. 285–293,
 A. Adler and R. Guardo, “Electrical impedance tomography: regularized
imaging and contrast detection,” IEEE Trans. Med. Imag., vol. 15, no. 2,
pp. 170–9, Jan. 1996.
 B. M. Graham and A. Adler, “Objective selection of hyperparameter
for EIT,” Physiological measurement, vol. 27, no. 5, pp. S65–79, May
 C. Gabriel, A. Peyman, and E. H. Grant, “Electrical conductivity of
tissue at frequencies below 1 MHz,” Physics in medicine and biology,
vol. 54, no. 16, pp. 4863–78, Aug. 2009.
 T. Dai, C. G´ omez-Laberge, and A. Adler, “Reconstruction of conductiv-
ity changes and electrode movements based on eit temporal sequences,”
Physiological measurement, vol. 29, no. 6, pp. S77–88, Jun. 2008.