Abstract #3978

NeXtQSM - A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with synthetic data

Francesco Cognolato^1,2, Kieran O’Brien^2,3, Jin Jin^2,3, Simon Robinson^4,5, Markus Barth^1,2,6, and Steffen Bollmann^1,2,6

¹Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, ²ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia, ³Siemens Healthcare Pty Ltd, Brisbane, Australia, ⁴High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, ⁵Department of Neurology, Medical University of Graz, Graz, Austria, ⁶School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

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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