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PISCO: Self-supervised k-space regularization for improved neural implicit k-space representations of dynamic MRI.
Med. Image Anal. 109:103890 (2025)
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 LPISCO, 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, making it a competitive dynamic reconstruction method without constraining the temporal resolution. Particularly at high acceleration factors (R ≥ 50), NIK with PISCO can avoid temporal oversmoothing of state-of-the-art methods and achieves superior spatio-temporal reconstruction quality. 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.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Dynamic Mri Reconstruction ; K-space Refinement ; Neural Implicit Representations ; Non-uniform Sampling ; Parallel Imaging ; Self-supervised Learning
ISSN (print) / ISBN
1361-8415
e-ISSN
1361-8415
Journal
Medical Image Analysis
Quellenangaben
Volume: 109,
Article Number: 103890
Publisher
Elsevier
Publishing Place
Radarweg 29, 1043 Nx Amsterdam, Netherlands
Reviewing status
Peer reviewed
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)
Grants
Federal Ministry of Research, Technology and Space
DAAD programme Konrad Zuse Schools of Excellence in Artificial Intelligence
Munich Center of Machine Learning
Add-on Fellowship of the Joachim Herz Foundation
Bayer Foundation Fellowship
Helmholtz Association under the joint research school "Munich School for Data Science-MUDS"
DAAD programme Konrad Zuse Schools of Excellence in Artificial Intelligence
Munich Center of Machine Learning
Add-on Fellowship of the Joachim Herz Foundation
Bayer Foundation Fellowship
Helmholtz Association under the joint research school "Munich School for Data Science-MUDS"