@article{article_650616, title={IMPEDANCE IMAGE RECONSTRUCTION WITH ARTIFICIAL NEURAL NETWORK IN ELECTRICAL IMPEDANCE TOMOGRAPHY}, journal={European Journal of Technique (EJT)}, volume={9}, pages={137–144}, year={2019}, DOI={10.36222/ejt.650616}, author={Kilic, Beyhan}, keywords={: electrical impedance tomography,finite element methods,biomedical image reconstruction,neural network}, abstract={<p class="TSAbstract" style="margin-bottom:6pt;"> <span lang="en-us" xml:lang="en-us">Electrical
impedance tomography views the electrical properties of the objects by
injecting current with surface electrodes and measuring voltages. Then using a
reconstructing algorithm, from the measured voltage-current values,
conductivity distribution of the object calculated. Finding internal
conductivity from surface voltage-current measurements is a reverse and
ill-posed problem. </span> </p> <p> </p> <p> </p> <p class="TSAbstract" style="margin-bottom:6pt;"> <span lang="en-us" xml:lang="en-us">Therefore,
high error sensitivity, and making approximations in conceiving complex
computations cause to limited spatial resolution. The classic iterative image
reconstruction algorithms have reconstruction errors. Accordingly, Electrical
impedance tomography images suffer low accuracy. It is necessary to evaluate
the collected data from the object surface with a new approach. In this paper,
the forward problem solved with the finite element method to reconstruct the
conductivity distribution inside the object, 
the reverse problem solved by the neural network approach. Image reconstruction
speed, conceptual simplicity, and ease of implementation maintained by  this approach. </span> </p> <p> </p>}, number={2}, publisher={Hibetullah KILIÇ}