Keywords: Image Reconstruction, Quantitative Susceptibility mapping
This study, SSMo-QSM, introduced a self-supervised strategy to achieve a model-based deep learning QSM reconstruction for dealing with the imperfect ground truth in supervised learning methods. In SSMo-QSM, the direct frequency domain division results between phase and dipole kernel at untruncated areas of TKD are randomly separated into two subsets. One is used as the data consistency of the unrolled network and the other is used to define the loss function for training, respectively. The preliminary results of synthetic data suggested that SSMo-QSM performed comparably against supervised methods on accurate susceptibility mapping with suppressed streaking artifact.
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