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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

Deep Learning provides powerful tools to address unsolved problems and unmet needs of MR-only Radiation Therapy Planning (RTP) in terms of synthetic CT conversion (required for acquired dose calculation) and time-consuming organ-at-risk (OAR) delineation.

Figure 1: In-phase ZTE without (top) and with 2xFOV extension plus DL image reconstruction (2^nd row) for head&neck, together with corresponding DL derived synthetic CT (3^rd row) and true CT (bottom row).

Figure 3: 2D T2 PROPELLER based automated OAR segmentation in the head & neck (i.e. brain, brainstem, eyes, lens, optic nerves, chiasm, pituitary gland, cochlea, parotid glands, mandible, oral cavity, submandibular glands, larynx, spinal cord, and body contour).