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Abstract #0194

Deep residual learning of radial under sampling artefacts for real-time MR image guidance during radiotherapy

Bjorn Stemkens1,2, Cheryl Sital1,2,3, Max Blokker1,2,4, Tom Bruijnen1, Jan JW Lagendijk1, Rob HN Tijssen1, and Cornelis AT van den Berg1

1Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands, 2MR Code B.V., Zaltbommel, Netherlands, 3Biomedical Engineering, University of Technology Eindhoven, Eindhoven, Netherlands, 4Medical Natural Sciences, VU Amsterdam, Amsterdam, Netherlands

MRI-guided radiotherapy using hybrid MR-Linac systems, requires high spatiotemporal resolution MR images to guide the radiation beam in real time. Here, we investigate the concept of deep residual learning of radial undersampling artifacts to decrease acquisition time and minimize extra reconstruction time by using the fast forward evaluation of the network. Within 8-10 milliseconds most streaking artifacts were removed for undersampling rates between R=4 and R=32 in the abdomen and brain, facilitating real-time tracking for MR-guided radiotherapy.

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