Abstract #3426

Large-scale simulation of rigid head motion artifacts in whole brain MRI data and the impact on automatic cortical segmentation

Hampus Olsson¹, Jason Michael Millward^1,2, Ludger Starke¹, Tobias Klein¹, Wenli Xu¹, Sabrina Klix¹, Christoph Lippert^3,4, Thoralf Niendorf^1,2, and Sonia Waiczies^1,2

¹Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, ²Experimental and Clinical Research Center, a joint cooperation between the Charitë Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, ³Digital Health & Machine Learning Research Group, Hasso Plattnet Institut for Digital Engineering, Potsdam, Germany, ⁴Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Synopsis

Keywords: Artifacts, Artifacts

MRI datasets from epidemiological studies such as the German National Cohort (GNC) are increasingly used in machine learning. These datasets show a lower prevalence of motion artifacts than encountered in clinical practice. We simulated realistic motion artifacts caused by rigid head motion on the GNC MR data (50 subjects) by modifying the TorchIO data augmentation framework. Five levels of artifact severity were simulated. We benchmarked our results to empirical measurements using the standard GNC MPRAGE imaging protocol. The robustness to motion of FreeSurfer and SynthSeg for cortical segmentation was investigated, indicating an improved performance using SynthSeg.

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