Application Of Electrical Impedance Tomography For Detecting Meat (Body Tissue): A Study On Frequency And Amplitude Variations - Aisya, Rohadatul; Samatha, Syifa Candiki; Ain, Khusnul; Astuti, Suryani Dyah (Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia, 2025)
Electrical Impedance Tomography (EIT) is an emerging non-invasive imaging technique with significant potential for detecting tissue anomalies; however, its performance is highly sensitive to variations in the frequency and amplitude of the injected electrical signals, which can lead to challenges in accurately differentiating between tissue types and detecting subtle pathological changes. This study aims to optimize EIT performance by systematically investigating the impact of signal frequency and amplitude on image reconstruction quality, thereby enhancing diagnostic accuracy. A portable multi-frequency EIT system was developed using Analog Discovery 2 and MATLAB, featuring a 16-electrode configuration arranged evenly around a tissue phantom, with beef tissue serving as an analog for human tissue due to its comparable conductivity properties. The experimental protocol varied signal amplitudes from 0.4 mA to 1.0 mA and frequencies from 50 kHz to 120 kHz, while two reconstruction algorithms the Gauss-Newton method and the GREIT algorithm were employed to evaluate image quality. Results demonstrated that the Gauss-Newton method achieved superior image clarity, with an approximate 18% improvement in reconstruction accuracy and a 20% reduction in noise at an optimal setting of 100 kHz frequency and 0.8 mA amplitude. Although the GREIT method provided faster reconstruction times, its lower sensitivity to amplitude variations resulted in less detailed anomaly detection. Overall, these findings underscore the critical importance of optimizing electrical parameters in EIT systems to enhance diagnostic capabilities. Future research should focus on integrating machine learning algorithms for real-time image processing and expanding the evaluation to include diverse tissue models to further improve the clinical applicability and robustness of EIT-based diagnostics.