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Current EIT analyses are based on the assumption of a circular thorax-shape and do not provide any information on lung borders. The aim is to obtain the body and lung border contours of male subjects by multi-detector computed tomography (MDCT) in defined thresholds of anthropometric data (gender = male; height; weight) for calibration of more realistic EIT reconstruction models.
A major drawback of EIT is its relatively poor spatial resolution and its limitation in measuring changes in bioimpedance as compared to a reference state (and not absolute quantities). Therefore, the technique cannot differentiate between extrapulmonary structures (muscles, thorax, heart, large vessels, spine, etc.) and non-aerated lung tissues - which is a major limitation for the clinical use of information derived from EIT-imaging. Moreover, current EIT-reconstruction algorithms are based on the consideration of a complete circular thoracic shape and do not take into account the body contours and lung borders.
The investigators are convinced that EIT-derived dynamic bedside lung imaging can be advanced by morphing computed tomography (CT) scans of the respective thoracic levels with concomitant EIT images - thus enhancing EIT-image information with CT-data. Integrating the anatomy of thoracic shape and lung borders provided by high-spatial resolution multi detector CT-scans (MDCT) with high-temporal resolution EIT has the potential to improve image quality considerably. This data can be used to compute mean EIT-reconstruction models that further offer the possibility to develop novel and clinically meaningful EIT parameters.
Therefore, the investigators hypothesize that by integration of CT-scan information of body and lung contours (and by computing different EIT reconstruction models) the current methodological limitations of EIT technology can be overcome.
Intervention Model: Single Group Assignment, Masking: Open Label, Primary Purpose: Diagnostic
"electrical impedance tomography"
Not yet recruiting
Medical University of Vienna
Published on BioPortfolio: 2016-05-18T10:31:09-0400
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