PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
Journal:
arXiv
Published Date:
Jan 16, 2025
Abstract
Neural implicit k-space representations (NIK) have shown promising results
for dynamic magnetic resonance imaging (MRI) at high temporal resolutions. Yet,
reducing acquisition time, and thereby available training data, results in
severe performance drops due to overfitting. To address this, we introduce a
novel self-supervised k-space loss function $\mathcal{L}_\mathrm{PISCO}$,
applicable for regularization of NIK-based reconstructions. The proposed loss
function is based on the concept of parallel imaging-inspired self-consistency
(PISCO), enforcing a consistent global k-space neighborhood relationship
without requiring additional data. Quantitative and qualitative evaluations on
static and dynamic MR reconstructions show that integrating PISCO significantly
improves NIK representations. Particularly for high acceleration factors
(R$\geq$54), NIK with PISCO achieves superior spatio-temporal reconstruction
quality compared to state-of-the-art methods. Furthermore, an extensive
analysis of the loss assumptions and stability shows PISCO's potential as
versatile self-supervised k-space loss function for further applications and
architectures. Code is available at:
https://github.com/compai-lab/2025-pisco-spieker
