Physics-Aware Motion Simulation For T2*-Weighted Brain MRI

Author:

Eichhorn Hannah,Hammernik Kerstin,Spieker Veronika,Epp Samira M.,Rueckert Daniel,Preibisch Christine,Schnabel Julia A.

Publisher

Springer Nature Switzerland

Reference24 articles.

1. Retrospective correction of motion in MR images. In: van der Kouwe, A.J., Andre, J.B. (eds.) Motion Correction in MR, vol. 6, pp. 259–267. Academic Press (2022)

2. Brackenier, Y., et al.: Data-driven motion-corrected brain MRI incorporating pose-dependent B0 fields. Magn. Reson. Med. 88(2), 817–831 (2022)

3. Chatterjee, S., Sciarra, A., Dünnwald, M., Oeltze-Jafra, S., Nürnberger, A., Speck, O.: Retrospective motion correction of MR images using prior-assisted deep learning. In: Proceedings of the 34th Conference on NeurIPS (2020)

4. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

5. Eichhorn, H., Hammernik, K., Epp, S.M., Karampinos, D.C., Schnabel, J.A., Preibisch, C.: Investigating the impact of motion and associated B0 changes on oxygenation sensitive MRI through realistic simulations. In: Proceedings of the International Society for Magnetic Resonance in Medicine, vol. 31 (2023)

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1. Deep learning for accelerated and robust MRI reconstruction;Magnetic Resonance Materials in Physics, Biology and Medicine;2024-07-23

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