Deep convolutional neural networks have recently gained popularity for solving the ill-posed dipole inversion problem in Quantitative Susceptibility Mapping (QSM). The training of the neural networks is performed with examples of χ and f that can either be obtained from physical simulations on synthetic source distributions, or through “classical” QSM methods on real data. For both choices, there is a plethora of decisions to make and parameters to set. Here we seek to present best practices regarding the modelling of synthetic source distributions and data augmentation.
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