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皮斯科:自我监督的k空间规范化,以改善动态MRI的神经隐性k空间表示

Veronika Spieker1, Hannah Eichhorn2, Wenqi Huang3

  • 1Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany; Millenium Institute for Intelligent Healthcare Engineering, Santiago, Chile.

Medical image analysis
|December 10, 2025
PubMed
概括

我们介绍了PISCO,一个自我监督的k空间损失函数,以改进动态MRI的神经隐性k空间表示 (NIK). 通过强制执行无需额外数据的k空间一致性,PISCO提高了重建质量,特别是在高加速度因子时.

关键词:
动态MRI重建的动态MRI重建在K空间精细化中.神经隐含表示的神经隐含表示.不统一的抽样采集方式并行成像并行成像自主监督学习学习

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