Editorial
Editorial for “Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor and Multi-disease Study”
Amel Imene Hadj Bouzid PhD,
Amel Imene Hadj Bouzid PhD
Université Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, CIC 1401, Bordeaux, France
Inserm, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, CIC 1401, Bordeaux, France
Search for more papers by this author Gaël Dournes MD, PhD,
Corresponding Author
Gaël Dournes MD, PhD
Université Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, CIC 1401, Bordeaux, France
Inserm, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, CIC 1401, Bordeaux, France
CHU de Bordeaux, Service d'Imagerie Cardiaque et Thoracique et Cardiovasculaire, CIC 1401, Pessac, France
Search for more papers by this author
Amel Imene Hadj Bouzid PhD,
Amel Imene Hadj Bouzid PhD
Université Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, CIC 1401, Bordeaux, France
Inserm, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, CIC 1401, Bordeaux, France
Search for more papers by this author Gaël Dournes MD, PhD,
Corresponding Author
Gaël Dournes MD, PhD
Université Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, CIC 1401, Bordeaux, France
Inserm, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, CIC 1401, Bordeaux, France
CHU de Bordeaux, Service d'Imagerie Cardiaque et Thoracique et Cardiovasculaire, CIC 1401, Pessac, France
Search for more papers by this author
First published: 27 February 2023
No abstract is available for this article.
References
- 1Soffer S, Morgenthau AS, Shimon O, et al. Artificial intelligence for interstitial lung disease analysis on chest computed tomography: A systematic review. Acad Radiol 2022; 29: S226-S235.
- 2Roach DJ, Crémillieux Y, Serai SD, et al. Morphological and quantitative evaluation of emphysema in chronic obstructive pulmonary disease patients: A comparative study of MRI with CT. J Magn Reson Imaging 2016; 44: 1656-1663.
- 3Dournes G, Hall CS, Willmering MM, et al. Artificial intelligence in computed tomography for quantifying lung changes in the era of CFTR modulators. Eur Respir J 2022; 59:2100844.
- 4Astley JR, Biancardi AM, Hughes PJC, et al. Implementable deep learning for multi-sequence proton MRI lung segmentation: a multi-center, multivendor and multi-disease study. J Magn Reson Imaging 2023; 58: 1034-1048.
- 5Dournes G, Walkup LL, Benlala I, et al. The clinical use of lung MRI in cystic fibrosis: What, now, how? Chest 2021; 159: 2205-2217.
- 6Benlala I, Albat A, Blanchard E, et al. Quantification of MRI T2 interstitial lung disease signal-intensity volume in idiopathic pulmonary fibrosis: A pilot study. J Magn Reson Imaging 2021; 53: 1500-1507.
- 7Marshall H, Voskrebenzev A, Smith LJ, et al. 129 Xe and free-breathing 1 H ventilation MRI in patients with cystic fibrosis: A dual-center study. J Magn Reson Imaging 2023; 57: 1908-1921.
- 8Dournes G, Yazbek J, Benhassen W, et al. 3D ultrashort echo time MRI of the lung using stack-of-spirals and spherical k-space coverages: Evaluation in healthy volunteers and parenchymal diseases. J Magn Reson Imaging 2018; 48: 1489-1497.
- 9Willmering MM, Robison RK, Wang H, Pipe JG, Woods JC. Implementation of the FLORET UTE sequence for lung imaging. Magn Reson Med 2019; 82: 1091-1100.
- 10Benlala I, Point S, Leung C, et al. Volumetric quantification of lung MR signal intensities using ultrashort TE as an automated score in cystic fibrosis. Eur Radiol 2020; 30: 5479-5488.