Most the image reconstruction algorithms in magnetic resonance electrical impedance tomography (MREIT) and diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) require at least two independent current patterns to uniquely reconstruct conductivity distributions. However, in transcranial electrical current stimulation (tES) or deep brain stimulation (DBS) only one current injection data is available. We applied Kirchhoff’s voltage law (KVL) in a mimetic discretized network, additional current data obtained from a computational model, and a radial basis function artificial neural network (RBF-ANN) approach, to demonstrate that it is possible to reconstruct the conductivity images using a single experimental current administration.
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