Quantitative susceptibility mapping (QSM) aims to extract the magnetic susceptibility of tissue by solving an ill-posed field-to-source-inversion. Current QSM algorithms require manual parameter choices to balance between smoothing, artifacts and quantitation accuracy. Deep neural networks have been shown to perform well on ill-posed problems and can find optimal parameter sets for a given problem based on real-world training data. We have developed a proof-of-concept fully convolutional deep network capable of solving QSM’s ill-posed field-to-source inversion that preserves fine spatial structures and delivers accurate quantitation results.
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