Details

Autor(en) / Beteiligte

• Handayani, N
• Hidayanti, K F
• Baidillah, M R
• Arif, I
• Khotimah, S N
• Haryanto, F
• Taruno, W P

Titel

Simulation of Neural Network-Multicriterion Optimization Image Reconstruction Technique (NN-MOIRT) for imaging using a 32-channel Brain ECVT sensor

Ist Teil von

• Journal of physics. Conference series, 2019-01, Vol.1127 (1), p.12008

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2019

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• NN-MOIRT has been proposed earlier as an alternative for existing image reconstruction algorithms that can accurately show a volumetric image in a cylindrical sensor vessel. The Brain ECVT sensor has a different shape and dimensions compared with a common ECVT sensor, which has a cylindrical shape. Using a different sensor changes the image reconstruction algorithm parameters. Thus, the image reconstruction algorithm should be modified to be able to properly make a reconstruction. In this study, a simulation of the reconstruction of an image from a 32-channel Brain ECVT sensor using NN-MOIRT was conducted. The simulation was performed by varying the position and number of objects in the helmet-shaped Brain ECVT sensor. The alpha parameter (penalty factor) was varied from 10 to 150 with the number of iterations from 1 to 200. The RMSE (root mean square error) was calculated based on the difference between the permittivity distribution of the objects and the reconstructed image. It was found that the NN-MOIRT algorithm is more convergent and more stable for image reconstruction than the ILBP algorithm.