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E1854. Electrical Impedance Tomography: A New Frontier
Authors
  1. Michael Margron; Dartmouth Hitchcock Medical Center
  2. Alexander Lindqwister; Dartmouth Hitchcock Medical Center
  3. Sohum Patel; Dartmouth Hitchcock Medical Center
  4. Eric Hoffer; Dartmouth Hitchcock Medical Center
Background
Electrical impedance tomography (EIT) is an imaging technique that uses differences in tissue conductivity across an array of electrodes to quickly generate real time images. EIT was originally developed as an industrial imaging method, but since the 1980’s, has more recently been used experimentally in various medical applications. This new imaging modality has a wide variety of potential applications, particularly in dynamic imaging and assessment of physiologic characteristics. While EIT has several major drawbacks, including low spatial resolution and the influence of body habitus, major improvements to this approach have been made in both hardware development and with image reconstruction algorithms. As applications of EIT become more widely used, it will become imperative for clinical radiologists to become familiarized with this technology and its potential impact on clinical practice.

Educational Goals / Teaching Points
Describe the process by which EIT images are created, explain advantages and limitations of EIT, and identify clinical applications of this new imaging modality.

Key Anatomic/Physiologic Issues and Imaging Findings/Techniques
EIT has many applications across various fields including realtime monitoring during procedures or at the bedside, as well as a new metric of assessing tissue properties that can be additive to existing modalities. This has been explored in the detection of lung pathologies such as subtle or evolving pneumothoraces as well as pulmonary emboli, optimization of mechanical ventilation, and cardiac and peripheral perfusion. EIT has also found potential applications in breast cancer screening, gastric motility studies, characterization of abdominal lesions, and assessment of ablation efficacy. The potential for machine learning approaches to EIT imaging will also be discussed.

Conclusion
EIT has many potential applications and will likely become more widely prevalent in the near future. It is important for radiologists to recognize the strengths and limitations of this technique and its broad potential for next generation imaging.