Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914750564139008 |
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| author | Spieker, Veronika Eichhorn, Hannah Stelter, Jonathan K. Huang, Wenqi Braren, Rickmer F. Rückert, Daniel Costabal, Francisco Sahli Hammernik, Kerstin Prieto, Claudia Karampinos, Dimitrios C. Schnabel, Julia A. |
| author_facet | Spieker, Veronika Eichhorn, Hannah Stelter, Jonathan K. Huang, Wenqi Braren, Rickmer F. Rückert, Daniel Costabal, Francisco Sahli Hammernik, Kerstin Prieto, Claudia Karampinos, Dimitrios C. Schnabel, Julia A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08350 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation Spieker, Veronika Eichhorn, Hannah Stelter, Jonathan K. Huang, Wenqi Braren, Rickmer F. Rückert, Daniel Costabal, Francisco Sahli Hammernik, Kerstin Prieto, Claudia Karampinos, Dimitrios C. Schnabel, Julia A. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Signal Processing Medical Physics 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. |
| title | Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Signal Processing Medical Physics |
| url | https://arxiv.org/abs/2404.08350 |