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Mutual Information as a Measure of Image Quality for 3-D Dynamic Lung Imaging with EIT

1School of Mathematics, University of Manchester, UK.
2School of Electrical and Electronic Engineering, University of Manchester, UK.
3Centre for Imaging Sciences, Biomedical Imaging Institute, University of Manchester, UK.
4Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
IOP Publishing Ltd.
Publication Date:
Physiol Meas
Volume Number:
Issue Number:
Physiol Meas. 2014 May;35(5):863-79.
PubMed ID:
3D lung EIT, mutual information, image co-registration
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
Generated Citation:
Crabb M.G., Davidson J.L., Little R., Wright P., Morgan A.R., Miller C.A., Naish J.H., Parker G., Kikinis R., McCann H., Lionheart W. Mutual Information as a Measure of Image Quality for 3-D Dynamic Lung Imaging with EIT. Physiol Meas. 2014 May;35(5):863-79. PMID: 24710978. PMCID: PMC4059506.
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We report on a pilot study of dynamic lung electrical impedance tomography (EIT) at the University of Manchester. Low-noise EIT data at 100 frames per second were obtained from healthy male subjects during controlled breathing, followed by magnetic resonance imaging (MRI) subsequently used for spatial validation of the EIT reconstruction. The torso surface in the MR image and electrode positions obtained using MRI fiducial markers informed the construction of a 3D finite element model extruded along the caudal-distal axis of the subject. Small changes in the boundary that occur during respiration were accounted for by incorporating the sensitivity with respect to boundary shape into a robust temporal difference reconstruction algorithm. EIT and MRI images were co-registered using the open source medical imaging software, 3D Slicer. A quantitative comparison of quality of different EIT reconstructions was achieved through calculation of the mutual information with a lung-segmented MR image. EIT reconstructions using a linear shape correction algorithm reduced boundary image artifacts, yielding better contrast of the lungs, and had 10% greater mutual information compared with a standard linear EIT reconstruction.

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