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Abstract #0108

Real-time estimation of 2D deformation vector fields from highly undersampled, dynamic k-space for MRI-guided radiotherapy using deep learning

Maarten L Terpstra1,2, Federico d'Agata1,2,3, Bjorn Stemkens1,2, Jan JW Lagendijk1, Cornelis AT van den Berg1,2, and Rob HN Tijssen1,2
1Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, Netherlands, 2Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, 3Department of Neurosciences, University of Turin, Turin, Italy

MRI-guided radiotherapy (MRgRT) enables new ways to improve dose delivery to moving tumors and the organs-at-risk (e.g. in abdomen) by steering the radiation beam based on real-time MRI. While state-of-the-art techniques (e.g. compressed sensing) can provide the required acquisition speed, the corresponding reconstruction time is too long for real-time processing. In this work, we investigate the use of multiple deep neural networks for image reconstruction and subsequent motion estimation. We show that a single motion estimation network can estimate high-quality 2D deformation vector fields from aliased images, even for high undersampling factors up to R=25.

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