@inproceedings{2611b6da2367400daa1380c724e75d65,
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",
abstract = "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 (ABM). 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 ABM by 13% in term of MAE in whole pelvis. This method could be used for online dose calculation in MRI-only radiotherapy.",
keywords = "Magnetic resonance imaging, Pseudo-ct, Radiotherapy treatment planning",
author = "Axel Largent and Nunes, {Jean Claude} and Herve Saint-Jalmes and John Baxter and Peter Greer and Jason Dowling and Crevoisier, {Renaud De} and Oscar Acosta",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
doi = "10.1109/ISBI.2019.8759278",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1109--1113",
booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",
address = "United States",
}