Deep learning based quantitative susceptibility mapping has shown great potential in recent years, outperforming traditional non-learning approaches in speed and accuracy in many applications. Here we aim to overcome the limitations of in vivo training data and model-agnostic deep learning approaches commonly used in the field. We developed a new synthetic training data generation method that enables the background field correction and a data-consistent solution of the dipole inversion to be learned using a variational network in one pipeline. NeXtQSM is a complete deep learning based pipeline for computing robust, fast and accurate quantitative susceptibility maps.
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