Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks.

Christiane Posselt¹, Mehmet Yigit Avci², Mehmet Yigitsoy², Patrick Schuenke³, Christoph Kolbitsch³, Tobias Schaeffter^3,4, Stefanie Remmele¹

¹University of Applied Sciences, Faculty of Electrical and Industrial Engineering, Landshut, Germany.

²deepc GmbH, Munich, Germany.

³Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.

⁴Technical University of Berlin, Department of Medical Engineering, Berlin, Germany.

PubMed PMC DOI PDF

Abstract

PURPOSE: To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols. APPROACH: The approach simulates "acquisition shift derivatives" of MR images based on MR si…

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