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
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
NeXtQSM is a complete deep learning based pipeline trained on synthetic data for computing quantitative susceptibility maps including background field correction and a data-consistent dipole inversion.
Illustration of the NeXtQSM pipeline including the training data generation process (blue) and the two deep learning models trained jointly in one optimization (red). In the data generation, we apply the QSM forward operation to the synthetic brain with and without external sources to have the inputs for the two learning steps. In the learning part, the two architectures can be seen as a unique one because of the end-to-end training fashion.
Illustration of the training dataset. The left column shows the starting synthetic brain, the center shows the data after application of the QSM dipole model and on the right the data after applying the forward model including the effect of external sources.