  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.
