Bi-frequency symmetry difference EIT - feasibility and limitations of application to stroke diagnosis
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McDermott, B. J., Ohalloran, M., Avery, J., & Porter, E. (2019). Bi-Frequency Symmetry Difference EIT - Feasibility and Limitations of Application to Stroke Diagnosis. IEEE Journal of Biomedical and Health Informatics, doi: 10.1109/JBHI.2019.2960862
Objective: Bi-Frequency Symmetry Difference (BFSD)-EIT can detect, localize and identify unilateral perturbations in symmetric scenes. Here, we test the viability and robustness of BFSD-EIT in stroke diagnosis. Methods: A realistic 4-layer Finite Element Method (FEM) head model with and without bleed and clot lesions is developed. Performance is assessed with test parameters including: measurement noise, electrode placement errors, contact impedance errors, deviations in assumed tissue conductivity, deviations in assumed anatomy, and a frequency-dependent background. A final test is performed using ischemic patient data. Results are assessed using images and quantitative metrics. Results: BFSD-EIT may be feasible for stroke diagnosis if a signal-to-noise ratio (SNR) of ≥60dB is achievable. Sensitivity to errors in electrode positioning is seen with a tolerance of only ±5mm, but a tolerance of up to ±30mm is possible if symmetry is maintained between symmetrically opposite partner electrodes. The technique is robust to errors in contact impedance and assumed tissue conductivity up to at least ±50%. Asymmetric internal anatomy affects performance but may be tolerable for tissues with frequency-dependent conductivity. Errors in assumed external geometry marginally affect performance. A frequency-dependent background does not affect performance with carefully chosen frequency points or use of multiple frequency points across a band. The Global Left-Hand Side (LHS) & Right-Hand Side (RHS) Mean Intensity metric is particularly robust to errors. Conclusion: BFSD-EIT is a promising technique for stroke diagnosis, provided parameters are within the tolerated ranges. Significance: BFSD-EIT may prove an important step forward in imaging of static scenes such as stroke.
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