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.
@article{arxiv.2404.08350,
title = {Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation},
author = {Veronika Spieker and Hannah Eichhorn and Jonathan K. Stelter and Wenqi Huang and Rickmer F. Braren and Daniel Rückert and Francisco Sahli Costabal and Kerstin Hammernik and Claudia Prieto and Dimitrios C. Karampinos and Julia A. Schnabel},
journal= {arXiv preprint arXiv:2404.08350},
year = {2024}
}