  Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great
potential in recent years, obtaining similar results to established
non-learning approaches. Many current deep learning approaches are not data
consistent, require in vivo training data or solve the QSM problem in
consecutive steps resulting in the propagation of errors. Here we aim to
overcome these limitations and developed a framework to solve the QSM
processing steps jointly. We developed a new hybrid training data generation
method that enables the end-to-end training for solving background field
correction and dipole inversion in a data-consistent fashion using a
variational network that combines the QSM model term and a learned regularizer.
We demonstrate that NeXtQSM overcomes the limitations of previous deep learning
methods. NeXtQSM offers a new deep learning based pipeline for computing
quantitative susceptibility maps that integrates each processing step into the
training and provides results that are robust and fast.
