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Abstract #0321

Deep Learning for solving ill-posed problems in Quantitative Susceptibility Mapping – What can possibly go wrong?

Pia Christine Høy1, Kristine Storm Sørensen1, Lasse Riis Østergaard1, Kieran O'Brien2,3, Markus Barth2, and Steffen Bollmann2

1Department of Health Science and Technology, Aalborg University, Aalborg, Denmark, 2Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, 3Healthcare Pty Ltd, Siemens, Brisbane, Australia

Quantitative susceptibility mapping (QSM) aims to solve an ill-posed field-to-source inversion to extract magnetic susceptibility of tissue. QSM algorithms based on deep convolutional neural networks have shown to produce artefact-free susceptibility maps. However, clinical scans often have a large variability, and it is unclear how a deep learning-based QSM algorithm is affected by discrepancies between the training data and clinical scans. Here we investigated the effects of different B0 orientations and noise levels of the tissue phase on the final quantitative susceptibility maps.

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