Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI
  Using Neural Implicit k-Space Representation
Veronika Spieker Hannah Eichhorn Jonathan K. Stelter Wenqi Huang Rickmer F. Braren Daniel Rückert Francisco Sahli Costabal Kerstin Hammernik Claudia Prieto Dimitrios C. Karampinos Julia A. Schnabel
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.