- Title
- Pseudo-CT generation for MRI-only radiotherapy: comparative study between a generative adversarial network, a U-Net network, a patch-based, and an atlas based methods
- Creator
- Largent, Axel; Nunes, Jean-Claude; Saint-Jalmes, Hervé; Baxter, John; Greer, Peter; Dowling, Jason; de Crevoisier, Renaud; Acosta, Oscar
- Relation
- 2019 IEEE 16th International Symposium on Biomedical Imaging. 16th IEEE International Symposium on Biomedical Imaging (ISBI) (Venice, Italy 08-11 April, 2019) p. 1109-1113
- Publisher Link
- http://dx.doi.org/10.1109/ISBI.2019.8759278
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2019
- Description
- As new radiotherapy treatment systems using MRI (rather than traditional CT) are being developed, the accurate calculation of dose maps from MR imaging has become an increasing concern. MRI provides good soft-tissue but, unlike CT, lacks the electron density information necessary for dose calculation. In this paper, we proposed a generative adversarial network (GAN) using a perceptual loss to generate pseudo-CTs for prostate MRI dose calculation. This network was evaluated and compared to a U-Net network, a patch-based (PBM) and an atlas-based methods (ARM). Influence of the perceptual loss was assessed by comparing this network to a GAN using a L2 loss. GANs and U-Nets are rather similar with slightly better results for GANs. The proposed GAN outperformed the PBM by 9% and the ARM by 13% in term of MAE in whole pelvis. This method could be used for online dose calculation in MRI-only radiotherapy.
- Subject
- Pseudo-CT; magnetic resonance imaging; radiotherapy treatment planning; comparative study
- Identifier
- http://hdl.handle.net/1959.13/1429457
- Identifier
- uon:38715
- Identifier
- ISBN:9781538636404
- Rights
- © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
- Language
- eng
- Full Text
- Reviewed
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