Navegación

Búsqueda

Búsqueda avanzada

Resultados de búsqueda para Electrical Impedance Tomography

System for EIT reconstruction based on Machine Learning techniques

Electrical Impedance Tomography (EIT) is a non-invasive technique that can be used to obtain information from inside bodies. To reconstruct internal body images using EIT, it is necessary to solve a mathematical ill-posed problem called inverse problem. We have developed a software called SageTomo that is able to reconstruct EIT images using Machine Learning techniques to solve the inverse problem. Furthermore, SageTomo allows users both to train and store Machine Learning models for EIT reconstruction, as well as to generate and store datasets for training these models.

Autores: Martín Aller Domínguez / David Mera Pérez / José Manuel Cotos Yáñez / Ledicia Díaz Lago / 
Palabras Clave: eit - Electrical Impedance Tomography - Machine Learning

Towards a Fast and Accurate EIT Inverse Problem Solver: A Machine Learning Approach

Different industrial and medical situations require the non-invasive extraction of information from the inside of bodies. This is usually done through tomographic methods that generate images based oninternal body properties. However, the image reconstruction involves a mathematical inverse problem, which accurate resolution demands large computation time and capacity. In this paper we explore the use of Machine Learning to develop an accurate solver for reconstructing Electrical Impedance Tomography images on real-time. We compare the results with the Iterative Gauss-Newton and the Primal Dual Interior Point Method, which are both largely used and well-validated solvers. The approaches were compared from the qualitative as well as the quantitative viewpoints. The former was focused on correctly detecting the internal body features. The latter was based on accurately predicting internalproperty distributions. Experiments revealed that our approach achieved better accuracy and Cohen’s kappa coefficient (97.57% and 94.60% respectively) from the qualitative viewpoint. Moreover, it also obtained better quantitative metrics with a Mean Absolute Percentage Error of 18.28%. Experiments confirmed that Neural Networks algorithms can reconstruct internal body properties with high accuracy, so they would be able to replace more complex and slower alternatives.

Autores: Xosé Fernández-Fuentes / David Mera / Andrés Gómez / Ignacio Vidal-Franco / 
Palabras Clave: Artificial neural networks - Conductivity - Electrical Impedance Tomography - Inverse Problems - Machine Learning

No encuentra los resultados que busca? Prueba nuestra Búsqueda avanzada