Conference
| Title | A Hybrid Reconstruction Algorithm for Web.EIT: A Difference Electrical Impedance Tomography Simulation System Posted by Earl Ryan Aleluya |
| Authors | Carl Richard M. Dumdum ; Earl Ryan M. Aleluya ; Cherry Mae J. Galangque ; Stephen H. Haim ; Carl John Salaan |
| Publication date | 2020/04/23 |
| Conference | 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) |
| Publisher | IEEE |
| Abstract | Electrical Impedance Tomography (EIT) is a technique that uses the voltage measurements, due to the injected alternating electrical currents to the body, taken on the boundary to produce an image that represents the internal conductivity distribution of the body. This is a relatively new imaging technique that has the potential to be used for clinical applications. As of today, EIT is not yet used in medical facilities as an alternative to X-ray and the likes, but it has the potential for clinical applications as it is a non-invasive and non-intrusive type of imaging technique. EIT is not that stable and as accurate compared to techniques that use radiation, since electrical current travels through paths with lesser impedance. The reconstruction of the image from the surface voltage measurements is difficult and challenging because it is nonlinear in nature. This study proposes (i) an innovative imaging technique that uses an inverse solver coupled with Convolutional Neural Network (CNN) trained on simulated data, (ii) and a difference EIT simulation system (Web.EIT) that features the proposed algorithm. The algorithm is evaluated and compared to an existing inverse solver (onestep Gauss-Newton) using simulated and actual datasets. Results showed that the proposed method outperformed the existing inverse solver by 37.02% lesser mean squared error (MSE) and 73.14% greater structural similarity (SSIM) index. |
| Index terms / Keywords | electrical impedance tomography, medical imaging, convolutional neural network, U-Net, deep learning |
| DOI | https://doi.org/10.1109/HNICEM48295.2019.9073594 |
| URL | https://ieeexplore.ieee.org/document/9073594/references#references |