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PISCO: Self-supervised k-space regularization for improved neural implicit k-space representations of dynamic MRI.

Veronika Spieker¹, Hannah Eichhorn², Wenqi Huang³

¹Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany; Millenium Institute for Intelligent Healthcare Engineering, Santiago, Chile.

Medical Image Analysis

|December 10, 2025

Summary

We introduce PISCO, a self-supervised k-space loss function, to improve neural implicit k-space representations (NIK) for dynamic MRI. PISCO enhances reconstruction quality, especially at high acceleration factors, by enforcing k-space consistency without extra data.

Keywords:

Dynamic MRI reconstruction K-space refinement Neural implicit representations Non-uniform sampling Parallel imaging Self-supervised learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Signal Processing

Background:

Neural implicit k-space representations (NIK) show promise for dynamic MRI.
Reduced acquisition time leads to overfitting and performance degradation in NIK.

Purpose of the Study:

To introduce a novel self-supervised k-space loss function, L_PISCO, for NIK regularization.
To improve the performance of NIK-based dynamic MRI reconstructions, particularly under accelerated acquisition conditions.

Main Methods:

Developed a self-supervised k-space loss function, parallel imaging-inspired self-consistency (PISCO).
Enforced consistent global k-space neighborhood relationships within NIK.
Evaluated PISCO on static and dynamic MR reconstructions.

Main Results:

PISCO significantly improved NIK representations and dynamic MRI reconstruction quality.
Superior spatio-temporal reconstruction was achieved at high acceleration factors (R ≥ 50).
NIK with PISCO avoided temporal oversmoothing compared to state-of-the-art methods.

Conclusions:

PISCO is a versatile self-supervised k-space loss function for NIK.
It enhances dynamic MRI reconstruction without compromising temporal resolution.
PISCO demonstrates potential for broader applications in MRI reconstruction and other architectures.