Electrical impedance tomography (EIT) is relatively new functional imaging technique in which electrodes are placed on the surface of the body, and low amplitude, low frequency current is applied on the electrodes. This results in a voltage distribution on the electrodes which can then be measured. From these voltages, an image is formed that reflects the conductivity and/or permittivity distribution within the region of interest. Since EIT is a safe and portable technology with no damaging side effects, it is particularly suitable for long-term patient monitoring. This project is part of a long term goal to develop a substantially improved EIT system for imaging heart and lung function in real-time and diagnosing lung pathologies and monitoring their respective treatments. This proposal focuses on the exploration of innovations in the reconstruction algorithm and hardware design, and a study of the clinical usefulness of the reconstructions. A fully nonlinear algorithm for reconstructing conductivity and permittivity in a cross-sectional region will be developed and implemented. Currently, no direct nonlinear algorithms have been implemented for the reconstruction of permittivity data for EIT. The images may provide pivotal information for the diagnosis of pleural effusion, pneumothorax, and atelectasis. To date there are very few EIT devices in the commercial prototype state, and none that acquire the data needed for the innovations proposed here. Thus, an EIT system will be designed and built that acquires single-ended and phasic voltage data on both current-carrying and noncurrent-carrying electrodes. The algorithms will be tested on simulated and experimental data, including data from an experimental lung injury model in pigs that will be used to assess the applicability in a clinical setting.

Public Health Relevance

Improving EIT technology can benefit the public health by expediting the diagnosis of certain lung pathologies, and by providing a bedside monitoring and diagnosis technique for patients in the intensive care unit with acute respiratory distress syndrome (ARDS), a life-threatening condition with a death rate of 20 to 30 % in which inflammation of lungs and fluid in the air sacs (alveoli) lead to low blood oxygen levels. Side effects of the treatment for patients with ARDS, which is mechanical ventilation through an endotracheal tube, include ventilator-induced pneumonia and lung damage, both of which can be prevented provided the proper ventilator settings are applied. Studies indicate that EIT may be useful for determining the optimal ventilator settings, monitoring reinflation procedures, determining information about alveolar collapse, and diagnosis of pleural effusion, pneumothorax, and atelectasis.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZRG1-SBIB-U (92))
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Sastre, Antonio
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Colorado State University-Fort Collins
Biostatistics & Other Math Sci
Schools of Arts and Sciences
Fort Collins
United States
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Herrera, Claudia N L; Vallejo, Miguel F M; Mueller, Jennifer L et al. (2015) Direct 2-D reconstructions of conductivity and permittivity from EIT data on a human chest. IEEE Trans Med Imaging 34:267-74
Hamilton, Sarah J; Mueller, Jennifer L (2013) Direct EIT reconstructions of complex admittivities on a chest-shaped domain in 2-D. IEEE Trans Med Imaging 32:757-69
Hamilton, S J; Herrera, C N L; Mueller, J L et al. (2012) A direct D-bar reconstruction algorithm for recovering a complex conductivity in 2-D. Inverse Probl 28: