MR to CT Synthesis Using GANs: A Practical Guide Applied to Thoracic Imaging

Arthur Longuefosse, Baudouin Denis De Senneville, Gaël Dournes, Ilyes Benlala, François Laurent, Pascal Desbarats, Fabien Baldacci

2023

Abstract

In medical imaging, MR-to-CT synthesis has been extensively studied. The primary motivation is to benefit from the quality of the CT signal, i.e. excellent spatial resolution, high contrast, and sharpness, while avoiding patient exposure to CT ionizing radiation, by relying on the safe and non-invasive nature of MRI. Recent studies have successfully used deep learning methods for cross-modality synthesis, notably with the use of conditional Generative Adversarial Networks (cGAN), due to their ability to create realistic images in a target domain from an input in a source domain. In this study, we examine in detail the different steps required for cross-modality translation using GANs applied to MR-to-CT lung synthesis, from data representation and pre-processing to the type of method and loss function selection. The different alternatives for each step were evaluated using a quantitative comparison of intensities inside the lungs, as well as bronchial segmentations between synthetic and ground truth CTs. Finally, a general guideline for cross-modality medical synthesis is proposed, bringing together best practices from generation to evaluation.

Paper Citation

in Harvard Style

Longuefosse A., Denis De Senneville B., Dournes G., Benlala I., Laurent F., Desbarats P. and Baldacci F. (2023). MR to CT Synthesis Using GANs: A Practical Guide Applied to Thoracic Imaging. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP; ISBN 978-989-758-634-7, SciTePress, pages 268-274. DOI: 10.5220/0011895700003417

in Bibtex Style

@conference{ivapp23,
author={Arthur Longuefosse and Baudouin Denis De Senneville and Gaël Dournes and Ilyes Benlala and François Laurent and Pascal Desbarats and Fabien Baldacci},
title={MR to CT Synthesis Using GANs: A Practical Guide Applied to Thoracic Imaging},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP},
year={2023},
pages={268-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011895700003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP
TI - MR to CT Synthesis Using GANs: A Practical Guide Applied to Thoracic Imaging
SN - 978-989-758-634-7
AU - Longuefosse A.
AU - Denis De Senneville B.
AU - Dournes G.
AU - Benlala I.
AU - Laurent F.
AU - Desbarats P.
AU - Baldacci F.
PY - 2023
SP - 268
EP - 274
DO - 10.5220/0011895700003417
PB - SciTePress