Since tissue conductivity is determined by the numerical second derivative of the phase map, it is particularly susceptible to motion. This abstract investigates the application of deep-learning based methods for retrospective correction of motion artifacts to obtain suitable phase maps as input for conductivity reconstruction. Different types of motion were investigated in the framework of volunteer experiments, revealing that the applied motion correction was indeed capable of improving conductivity reconstruction.
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