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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