Meeting Banner
Abstract #3978

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

Francesco Cognolato1,2, Kieran O’Brien2,3, Jin Jin2,3, Simon Robinson4,5, Markus Barth1,2,6, and Steffen Bollmann1,2,6
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here