Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy

Background and Purpose: To enable accurate magnetic resonance imaging (MRI)-based dose calculations, synthetic computed tomography (sCT) images need to be generated. We aim at assessing the feasibility of dose calculations from MRI acquired with a heterogeneous set of imaging protocol for paediatric patients affected by brain tumours. Materials and methods: Sixty paediatric patients undergoing brain radiotherapy were included. MR imaging protocols varied among patients, and data heterogeneity was maintained in train/validation/test sets. Three 2D conditional generative adversarial networks (cGANs) were trained to generate sCT from T1-weighted MRI, considering the three orthogonal planes and its combination (multi-plane sCT). For each patient, median and standard deviation (sigma) of the three views were calculated, obtaining a combined sCT and a proxy for uncertainty map, respectively. The sCTs were evaluated against the planning CT in terms of image similarity and accuracy for photon and proton dose calculations. Results: A mean absolute error of 61 +/- 14 HU (mean +/- 1 sigma) was obtained in the intersection of the body contours between CT and sCT. The combined multi-plane sCTs performed better than sCTs from any single plane. Uncertainty maps highlighted that multi-plane sCTs differed at the body contours and air cavities. A dose difference of -0.1 +/- 0.3% and 0.1 +/- 0.4% was obtained on the D > 90% of the prescribed dose and mean gamma(2%; 2 mm) pass-rate of 99.5 +/- 0.8% and 99.2 +/- 1.1% for photon and proton planning, respectively. Conclusion: Accurate MR-based dose calculation using a combination of three orthogonal planes for sCT generation is feasible for paediatric brain cancer patients, even when training on a heterogeneous dataset. (C) 2020 The Author(s). Published by Elsevier B.V.