Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
This work addresses motion artifacts in abdominal MRI for medical imaging applications, representing an incremental improvement over existing neural implicit methods.
The paper tackled the problem of limited regularization options for neural implicit k-space representations in dynamic MRI by introducing a self-supervised k-space regularization method called PISCO, which improved reconstructions on simulated data and enhanced spatio-temporal image quality in abdominal in-vivo scans compared to state-of-the-art methods.
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.