Time series of EIT chest images using singular value decomposition and Fourier transform

Area of Science:

• Medical Imaging
• Physiological Monitoring
• Biomedical Engineering

Background:

• Assessing regional ventilation and perfusion in the chest is crucial for understanding respiratory and cardiovascular health.
• Existing methods may have limitations in real-time, in vivo analysis of dynamic physiological processes.

Purpose of the Study:

• To propose and evaluate novel methods for exploring regional ventilation and perfusion in the chest using electrical impedance tomography (EIT).
• To demonstrate the utility of Fourier transform (FT) and singular value decomposition (SVD) based analyses for interpreting EIT data.

Main Methods:

• The study employed two primary analytical approaches: singular value decomposition (SVD) and Fourier transform (FT).
• Data were acquired from healthy volunteers using in vivo EIT.
• Specific analyses included the generation of power spectral density (PSD) and phase images from FT data.

Main Results:

• The developed methods provide a useful approach for exploring regional ventilation and perfusion.
• Power spectral density (PSD) and phase images derived from the Fourier transform were found to be more interpretable.
• These techniques facilitate the exploitation of in vivo EIT data for physiological assessment.

Conclusions:

• Fourier transform-derived power spectral density and phase images are valuable tools for analyzing EIT data.
• The proposed methods enhance the ability to explore cardiovascular and respiratory system dynamics in healthy individuals.
• EIT analysis using FT shows significant potential for non-invasive physiological monitoring.