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Abstract #4248

Deep Learning-based MR-only Radiation Therapy Planning for Head&Neck and Pelvis

Florian Wiesinger1, Sandeep Kaushik1, Mathias Engström2, Mika Vogel1, Graeme McKinnon3, Maelene Lohezic1, Vanda Czipczer4, Bernadett Kolozsvári4, Borbála Deák-Karancsi4, Renáta Czabány4, Bence Gyalai4, Dorottya Hajnal4, Zsófia Karancsi4, Steven F. Petit5, Juan A. Hernandez Tamames5, Marta E. Capala5, Gerda M. Verduijn5, Jean-Paul Kleijnen5, Hazel Mccallum6, Ross Maxwell6, Jonathan J. Wyatt6, Rachel Pearson6, Katalin Hideghéty7, Emőke Borzasi7, Zsófia Együd7, Renáta Kószó7, Viktor Paczona7, Zoltán Végváry7, Suryanarayanan Kaushik3, Xinzeng Wang3, Cristina Cozzini1, and László Ruskó4
1GE Healthcare, Munich, Germany, 2GE Healthcare, Stockholm, Sweden, 3GE Healthcare, Waukesha, WI, United States, 4GE Healthcare, Budapest, Hungary, 5Erasmus MC, Rotterdam, Netherlands, 6Newcastle University, Newcastle, United Kingdom, 7University of Szeged, Szeged, Hungary

MR imaging offers unique advantages for Radiation Therapy Planning (RTP) via excellent soft-tissue contrast for the delineation of the tumor target volume and surrounding organs-at-risk (OARs). Remaining challenges include absent CT information (required for accurate dose calculation) and time-consuming manual tumor and OAR contouring. Here we describe the application of Deep Learning for MR-only RTP in terms of synthetic CT conversion and automated OAR delineation. Exemplary results are illustrated from an ongoing MR-only RTP study in head&neck and pelvis.

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