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Objectives: Present a novel deep learning-based skull stripping algorithm formagnetic resonance imaging (MRI) that works directly in the information richk-space.  Materials and Methods: Using two datasets from different institutions with atotal of 36,900 MRI slices, we trained a deep learning-based model to workdirectly with the complex raw k-space data. Skull stripping performed by HD-BET(Brain Extraction Tool) in the image domain were used as the ground truth.  Results: Both datasets were very similar to the ground truth (DICE scores of92\%-98\% and Hausdorff distances of under 5.5 mm). Results on slices above theeye-region reach DICE scores of up to 99\%, while the accuracy drops in regionsaround the eyes and below, with partially blurred output. The output of k-stripoften smoothed edges at the demarcation to the skull. Binary masks are createdwith an appropriate threshold.  Conclusion: With this proof-of-concept study, we were able to show thefeasibility of working in the k-space frequency domain, preserving phaseinformation, with consistent results. Future research should be dedicated todiscovering additional ways the k-space can be used for innovative imageanalysis and further workflows.

k-strip: A novel segmentation algorithm in k-space for the application of skull stripping