A parametric model of the relationship between EIT and total lung volume

Area of Science:

• Physiology
• Medical Imaging
• Biomedical Engineering

Background:

• Electrical impedance tomography (EIT) offers a non-invasive method for monitoring lung function.
• Standardizing EIT image interpretation across individuals and conditions remains a challenge.
• Relating EIT data to quantitative physiological measures like lung volume is crucial for clinical application.

Purpose of the Study:

• To develop and validate a model that correlates EIT image contrast variations with lung volume changes.
• To investigate the influence of posture, gender, and anthropometric factors on this relationship.
• To establish a framework for inter-individual comparisons of EIT data.

Main Methods:

• Collected simultaneous spirometry and EIT data from 26 healthy subjects across various postures and breathing modes.
• Developed a quadratic model linking spirometry-measured lung volume changes (DeltaV(P)) to EIT contrast changes.
• Validated the model using independent data subsets, assessing accuracy through estimated vs. measured volume changes.

Main Results:

• A quadratic equation accurately modeled the relationship between lung volume changes and EIT contrast.
• Model coefficients were significantly influenced by posture, gender, thoracic circumference, and scapular skin fold.
• Validation demonstrated mean standard errors for lung volume estimation ranging from 9.3% to 12.4%.

Conclusions:

• The developed model provides a quantitative link between EIT images and lung volumes.
• It incorporates key individual factors (anthropometry, gender, posture) for improved EIT analysis.
• This represents a significant step towards standardized, inter-individual EIT image interpretation.