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Abstract #3596

Deep-learning based motion correction for brain conductivity reconstruction

Jan Hendrik Wuelbern1, Ulrich Katscher1, Karsten Sommer1, Axel Saalbach1, and Jalal B Andre2
1Philips Research Europe, Hamburg, Germany, 2University of Washington, Seattle, WA, United States

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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