Electrical Impedance Tomography Image Reconstruction Based on Neural Networks

Autor(es)

Bianchessi, Andre ; Akamine, Rodrigo H. ; Duran, Guilherme C. ; Tanabi, Naser ; Sato, Andre K. ; Martins, Thiago C. ; Tsuzuki, Marcos S. G.

• Tipo de documento: Artigo Científico
• Fonte: IFAC PAPERSONLINE; v. 53, n. 2, p. 6-pg., 2020-01-01.

Resumo

Electrical impedance tomography (EIT) is an imaging technique with a promising future. Several methods have been used for EIT image reconstruction, such as Simulated Annealing, Gauss Newton, Kalman filter and D-Bar. Recently, some authors solved this problem using artificial neural network (ANN) through pixel by pixel reconstruction, considering a fixed resolution for the final image. This work proposes a reconstruction based on the EIT forward problem. Two different meshes were considered: a coarse and a refined mesh. The latter was used to produce simulated potentials, which are the inputs for ANN training. The nodes conductivities, which used to create the outputs for training, defined in the coarser mesh. Therefore, the proposed method consists of training the ANN with inputs from a refined mesh and outputs from a coarse mesh. Two ANN architectures are proposed and compared: one based on the LeNet architecture, and another based on the feed-forward fully connected ANN. The obtained image is not dependent on any image resolution. The preliminary results show that the LeNet architecture has better performance. Copyright (C) 2020 The Authors. (AU)

• Processo FAPESP: 18/10549-3 - Aplicação de aprendizado supervisionado para a reconstrução de imagens por tomografia por impedância elétrica
• Beneficiário: Rodrigo Heira Akamine
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 17/07799-5 - Recozimento simulado intervalar implementado em GPGPU para obter imagens absolutas em tomografia por impedância elétrica
• Beneficiário: Marcos de Sales Guerra Tsuzuki
• Modalidade de apoio: Auxílio à Pesquisa - Regular