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.

翻译：目的：提出一种基于深度学习的新型颅骨剥离算法，该算法直接作用于信息丰富的k空间域，适用于磁共振成像（MRI）。材料与方法：利用来自不同机构的两组数据集（共36,900张MRI切片），训练了一个直接处理原始复数k空间数据的深度学习模型。以图像域中HD-BET（脑提取工具）完成的颅骨剥离结果作为金标准。结果：两组数据集的输出与金标准高度一致（DICE评分92%-98%，豪斯多夫距离小于5.5毫米）。眼部以上区域的切片DICE评分可达99%，而眼部及以下区域的精度下降，输出部分模糊。k-strip的输出在颅骨边界处常呈现平滑边缘。通过设定适当阈值生成二值掩膜。结论：通过这项概念验证研究，我们证明了在保留相位信息的k空间频域中工作的可行性，且结果一致。未来研究应致力于探索利用k空间进行创新图像分析及拓展工作流程的更多途径。