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In Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology

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 (σ) 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±1σ) 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 γ2%,2mm 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.

Maspero Matteo, Bentvelzen Laura G, Savenije Mark Hf, Guerreiro Filipa, Seravalli Enrica, Janssens Geert O, van den Berg Cornelis At, Philippens Marielle Ep

2020-Sep-22

Artificial intelligence, Brain tumors, Deep learning, Image-to-image translation, Machine learning., Pediatric oncology, Radiotherapy, Synthetic CT