The goal in electrical impedance tomography is to obtain the electrical properties of different materials by applying an electrical current and measuring the resulting potential difference at the boundaries of the domain. While the numerical accuracy is technically limited by the size of the elements within the finite-element (FE) mesh, using a fine mesh will result in a computationally demanding reconstruction, especially when the region of interest (ROI) is not known. However, this situation is different when the location is known, when one can easily refine the FE model around the target, aiming for greater accuracy around the ROI. In this paper, an innovative approach estimates the location of the target object before solving the inverse problem, so that it becomes possible to refine only a specific area of the FE model. A powerful artificial intelligence method is used to obtain this region.
- Electrical impedance tomography (EIT)
- finite element (FE)
- inverse problem
- machine learning