  Objectives: Present a novel deep learning-based skull stripping algorithm for
magnetic resonance imaging (MRI) that works directly in the information rich
k-space.
  Materials and Methods: Using two datasets from different institutions with a
total of 36,900 MRI slices, we trained a deep learning-based model to work
directly 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 of
92\%-98\% and Hausdorff distances of under 5.5 mm). Results on slices above the
eye-region reach DICE scores of up to 99\%, while the accuracy drops in regions
around the eyes and below, with partially blurred output. The output of k-strip
often smoothed edges at the demarcation to the skull. Binary masks are created
with an appropriate threshold.
  Conclusion: With this proof-of-concept study, we were able to show the
feasibility of working in the k-space frequency domain, preserving phase
information, with consistent results. Future research should be dedicated to
discovering additional ways the k-space can be used for innovative image
analysis and further workflows.
