Abstract
Variational Networks are a common approach in deep learning-based accelerated MR reconstruction. Due to their architecture, they may however fail in enforcing data consistency.We propose an adjustment to the Variational Network, integrating an optimization block that ensures consistency with the measured kspace points. We show the superiority of the method for zero-shot self-supervised 3D reconstruction quantitatively on retrospectively undersampled knee-data, and qualitatively in prospectively undersampled MR angiography images.
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References
Yaman B, Hosseini SAH, Moeller S, Ellermann J, Uğurbil K, Akçakaya M. Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson Med. 2020;84(6):3172–91.
Yaman B, Hosseini SAH, Akçakaya M. Zero-shot self-supervised learning for MRI reconstruction. ArXiv. 2021.
Aggarwal HK, Mani MP, Jacob M. MoDL: Model-based deep learning architecture for inverse problems. IEEE Trans Med Imaging. 2018;38(2):394–405.
Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med. 2018;79(6):3055–71.
Sriram A, Zbontar J, Murrell T, Defazio A, Zitnick CL, Yakubova N et al. End-to-end variational networks for accelerated MRI reconstruction. Med Image Comput Comput Assist Interv. Springer. 2020:64–73.
Lustig M, Donoho DL, Santos JM, Pauly JM. Compressed sensing MRI. IEEE Signal Process Mag. 2008;25(2):72–82.
Yaman B, Gu H, Hosseini SAH, Demirel OB, Moeller S, Ellermann J et al. Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging. NMR Biomed. 2022;35(12):e4798.
Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM et al. ESPIRiT: an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med. 2014;71(3):990–1001.
Epperson K, Sawyer AM, Lustig M, Alley M, Uecker M. Creation of fully sampled MR data repository for compressed sensing of the knee. Proc Sec Mag Reson Techn. 2013.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Fürnrohr, F., Wetzl, J., Vornehm, M., Giese, D., Knoll, F. (2024). Data Consistent Variational Networks for Zero-shot Self-supervised MR Reconstruction. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_81
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DOI: https://doi.org/10.1007/978-3-658-44037-4_81
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