Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
<|reference_start|>Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation: Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application of common image regularization methods to improve the final reconstruction. In this work, we introduce the concept of parallel imaging-inspired self-consistency (PISCO), which we incorporate as novel self-supervised k-space regularization enforcing a consistent neighborhood relationship. At no additional data cost, the proposed regularization significantly improves neural implicit k-space reconstructions on simulated data. Abdominal in-vivo reconstructions using PISCO result in enhanced spatio-temporal image quality compared to state-of-the-art methods. Code is available at https://github.com/vjspi/PISCO-NIK.<|reference_end|>
@article{spieker2024self-supervised,
    title={Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI
  Using Neural Implicit k-Space Representation},
    author={Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang,
  Rickmer F. Braren, Daniel R"uckert, Francisco Sahli Costabal, Kerstin
  Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel},
    journal={arXiv preprint arXiv:2404.08350},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08350},
    primaryClass={eess.IV cs.CV cs.LG eess.SP physics.med-ph}
}