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Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy.
- Source :
-
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology [Radiother Oncol] 2020 Dec; Vol. 153, pp. 197-204. Date of Electronic Publication: 2020 Sep 23. - Publication Year :
- 2020
-
Abstract
- 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.<br />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.<br />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 γ <subscript>2%,2mm</subscript> pass-rate of 99.5 ± 0.8% and 99.2 ± 1.1% for photon and proton planning, respectively.<br />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.<br />Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1879-0887
- Volume :
- 153
- Database :
- MEDLINE
- Journal :
- Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
- Publication Type :
- Academic Journal
- Accession number :
- 32976877
- Full Text :
- https://doi.org/10.1016/j.radonc.2020.09.029