bibtext: @InProceedings{ Spi_SelfSupervised_MICCAI2024,
                   author = { Spieker, Veronika and Eichhorn, Hannah and Stelter, Jonathan K. and Huang, Wenqi and Braren, Rickmer F. and Rueckert, Daniel and Sahli Costabal, Francisco and Hammernik, Kerstin and Prieto, Claudia and Karampinos, Dimitrios C. and Schnabel, Julia A. },
                   title = { { Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 available at ***.git.
title: Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations
authors: Spieker, Veronika Eichhorn, Hannah Stelter, Jonathan K. Huang, Wenqi Braren, Rickmer F. Rueckert, Daniel Sahli Costabal, Francisco Hammernik, Kerstin Prieto, Claudia Karampinos, Dimitrios C. Schnabel, Julia A.