PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912190834933760 |
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| author | Spieker, Veronika Eichhorn, Hannah Huang, Wenqi Stelter, Jonathan K. Catalan, Tabita Braren, Rickmer F. Rueckert, Daniel Costabal, Francisco Sahli Hammernik, Kerstin Karampinos, Dimitrios C. Prieto, Claudia Schnabel, Julia A. |
| author_facet | Spieker, Veronika Eichhorn, Hannah Huang, Wenqi Stelter, Jonathan K. Catalan, Tabita Braren, Rickmer F. Rueckert, Daniel Costabal, Francisco Sahli Hammernik, Kerstin Karampinos, Dimitrios C. Prieto, Claudia Schnabel, Julia A. |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI Spieker, Veronika Eichhorn, Hannah Huang, Wenqi Stelter, Jonathan K. Catalan, Tabita Braren, Rickmer F. Rueckert, Daniel Costabal, Francisco Sahli Hammernik, Kerstin Karampinos, Dimitrios C. Prieto, Claudia Schnabel, Julia A. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Signal Processing Medical Physics 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 |
| title | PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRI |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Signal Processing Medical Physics |
| url | https://arxiv.org/abs/2501.09403 |