In this study, we designed a deep neural network that functions as dipole deconvolution in QSM reconstruction. For label data, COSMOS reconstructed QSM maps were used so that the network produces ground truth like COSMOS results without streaking artifacts. The performance of our network was superior to conventional QSM results with lower RMSE for multiple head orientation input data.
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