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Abstract #0859

A generic framework for real-time 3D motion estimation from highly undersampled k-space using deep learning

Maarten Terpstra1,2, Matteo Maspero1,2, Tom Bruijnen1,2, Joost Verhoeff1, Jan Lagendijk1, and Cornelis A.T. van den Berg1,2
1Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands, 2Computational Imaging Group for MR diagnostics & therapy, University Medical Center Utrecht, Utrecht, Netherlands

Motion estimation from MRI is important for image-guided radiotherapy. Specifically, for online adaptive MR-guided radiotherapy, the motion fields need to be available with high temporal resolution and a low latency. To achieve the required speed, MR acquisition is generally heavily accelerated, which results in image artifacts. Previously we have presented a deep learning method for real-time motion estimation in 2D that is able to resolve image artifacts. Here, we extend this method to 3D by training on prospectively undersampled respiratory-resolved data showing that our method produces high-quality motion fields at R=30 and even generalizes to CT without retraining.

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