Head-neck, lung and breast cancer patients acquired between 2016-2018.
The values are reported in RL, AP, FH directions.
Acquired on Synergy Linac with XVI (v5.0.2b72 Elekta AB, Sweden)
Acquired on Brilliance Big Bore (Philips Healthcare, Ohio, USA).
For each subject, each CT was translated and resampled to CBCT to minimise set-up errors.
For parameter files see the Elastix Model Zoo repository on GitHub.
elastix version: 4.700
The translation used to register CT to CBCT in all subjects
A fixed image mask was always used, given by the FOV of the CBCT, called Cylinder.gipl
Command line call:
elastix -f Fixed_CBCT.gipl -m Moving_CT.gipl -out ./ -mMask Cylinder.gipl -fMask Cylinder.gipl -p par0058trans.txt
Maspero M, Houweling AC, Savenije MH, van Heijst TC, Verhoeff JJ, Kotte AN, van den Berg CA. A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer. Physics and Imaging in Radiation Oncology. 2020 Apr 1;14:24-31. doi:https://doi.org/10.1016/j.phro.2020.04.002;
Maspero M, Savenije MH, van Heijst TC, Verhoeff JJ, Kotte AN, Houweling AC, van den Berg CA. CBCT-to-CT synthesis with a single neural network for head-and-neck, lung and breast cancer adaptive radiotherapy. arXiv preprint arXiv:1912.11136. 2019 Dec 23. arXiv:https://arxiv.org/abs/1912.11136v1.
© 2020 Viktor van der Valk