Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations

Veronika Spieker; Hannah Eichhorn; Jonathan K. Stelter; Wenqi Huang; Rickmer F. Braren; Daniel Rueckert; Francisco Sahli Costabal; Kerstin Hammernik; Claudia Prieto; Dimitrios C. Karampinos; Julia A. Schnabel

doi:10.1007/978-3-031-72104-5_59

Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations

Veronika Spieker^*, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rueckert, Francisco Sahli Costabal, Kerstin Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel

^*Corresponding author for this work

Biomedical Engineering Department

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

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/compai-lab/2024-miccai-spieker.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings
Editors	Marius George Linguraru, Qi Dou, Aasa Feragen, Stamatia Giannarou, Ben Glocker, Karim Lekadir, Julia A. Schnabel
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	614-624
Number of pages	11
ISBN (Print)	9783031721038
DOIs	https://doi.org/10.1007/978-3-031-72104-5_59
Publication status	Published - 2024
Event	27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 - Marrakesh, Morocco Duration: 6 Oct 2024 → 10 Oct 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15007 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
Country/Territory	Morocco
City	Marrakesh
Period	6/10/2024 → 10/10/2024

Keywords

Dynamic MRI Reconstruction
Implicit Neural Representations
k-Space Refinement
Parallel Imaging
Self-Supervised Learning

Access to Document

10.1007/978-3-031-72104-5_59

Cite this

Spieker, V., Eichhorn, H., Stelter, J. K., Huang, W., Braren, R. F., Rueckert, D., Sahli Costabal, F., Hammernik, K., Prieto, C., Karampinos, D. C., & Schnabel, J. A. (2024). Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations. In M. G. Linguraru, Q. Dou, A. Feragen, S. Giannarou, B. Glocker, K. Lekadir, & J. A. Schnabel (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings (pp. 614-624). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15007 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-72104-5_59

Spieker, Veronika ; Eichhorn, Hannah ; Stelter, Jonathan K. et al. / Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings. editor / Marius George Linguraru ; Qi Dou ; Aasa Feragen ; Stamatia Giannarou ; Ben Glocker ; Karim Lekadir ; Julia A. Schnabel. Springer Science and Business Media Deutschland GmbH, 2024. pp. 614-624 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations",

abstract = "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/compai-lab/2024-miccai-spieker.",

keywords = "Dynamic MRI Reconstruction, Implicit Neural Representations, k-Space Refinement, Parallel Imaging, Self-Supervised Learning",

author = "Veronika Spieker and Hannah Eichhorn and Stelter, {Jonathan K.} and Wenqi Huang and Braren, {Rickmer F.} and Daniel Rueckert and {Sahli Costabal}, Francisco and Kerstin Hammernik and Claudia Prieto and Karampinos, {Dimitrios C.} and Schnabel, {Julia A.}",

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Spieker, V, Eichhorn, H, Stelter, JK, Huang, W, Braren, RF, Rueckert, D, Sahli Costabal, F, Hammernik, K, Prieto, C, Karampinos, DC & Schnabel, JA 2024, Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations. in MG Linguraru, Q Dou, A Feragen, S Giannarou, B Glocker, K Lekadir & JA Schnabel (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15007 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 614-624, 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024, Marrakesh, Morocco, 6/10/2024. https://doi.org/10.1007/978-3-031-72104-5_59

Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations. / Spieker, Veronika; Eichhorn, Hannah; Stelter, Jonathan K. et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings. ed. / Marius George Linguraru; Qi Dou; Aasa Feragen; Stamatia Giannarou; Ben Glocker; Karim Lekadir; Julia A. Schnabel. Springer Science and Business Media Deutschland GmbH, 2024. p. 614-624 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15007 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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AU - Huang, Wenqi

AU - Braren, Rickmer F.

AU - Rueckert, Daniel

AU - Sahli Costabal, Francisco

AU - Hammernik, Kerstin

AU - Prieto, Claudia

AU - Karampinos, Dimitrios C.

AU - Schnabel, Julia A.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

PY - 2024

Y1 - 2024

N2 - 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/compai-lab/2024-miccai-spieker.

AB - 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/compai-lab/2024-miccai-spieker.

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KW - k-Space Refinement

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DO - 10.1007/978-3-031-72104-5_59

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BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings

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Spieker V, Eichhorn H, Stelter JK, Huang W, Braren RF, Rueckert D et al. Self-supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representations. In Linguraru MG, Dou Q, Feragen A, Giannarou S, Glocker B, Lekadir K, Schnabel JA, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 614-624. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-72104-5_59