Real-time style transfer for quantitative susceptibility mapping using unsupervised learning

Existing supervised deep learning methods for quantitative susceptibility mapping (QSM) could lead to degraded results when applied to phase images acquired with different scan parameters, such as image resolution and acquisition orientation. This work proposes a novel unsupervised learning method incorporating style transfer and deep image prior to enabling the reconstruction of susceptibility maps from local field maps acquired with any scan parameters. To speed up the inference, a pre-training strategy is also proposed, reducing reconstruction time from minutes to seconds.