MR to CT synthesis with multicenter data ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
MR to CT synthesis with multicenter data in the pelvic area using a conditional generative adversarial network
Auteur(s) :
Brou Boni, Kévin [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre Régional de Lutte contre le Cancer Oscar Lambret [Lille] [UNICANCER/Lille]
Klein, John [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Vanquin, Ludovic [Auteur]
Wagner, Antoine [Auteur]
Lacornerie, Thomas [Auteur]
Pasquier, David [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre Régional de Lutte contre le Cancer Oscar Lambret [Lille] [UNICANCER/Lille]
Reynaert, Nick [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre Régional de Lutte contre le Cancer Oscar Lambret [Lille] [UNICANCER/Lille]
Klein, John [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Vanquin, Ludovic [Auteur]
Wagner, Antoine [Auteur]
Lacornerie, Thomas [Auteur]
Pasquier, David [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre Régional de Lutte contre le Cancer Oscar Lambret [Lille] [UNICANCER/Lille]
Reynaert, Nick [Auteur]
Titre de la revue :
Physics in Medicine and Biology
Pagination :
075002
Éditeur :
IOP Publishing
Date de publication :
2020-04-01
ISSN :
0031-9155
Discipline(s) HAL :
Sciences de l'ingénieur [physics]
Résumé en anglais : [en]
The establishment of an MRI-only workflow in radiotherapy depends on the ability to generate an accurate synthetic CT (sCT) for dose calculation. Previously proposed methods have used a Generative Adversarial Network (GAN) ...
Lire la suite >The establishment of an MRI-only workflow in radiotherapy depends on the ability to generate an accurate synthetic CT (sCT) for dose calculation. Previously proposed methods have used a Generative Adversarial Network (GAN) for fast sCT generation in order to simplify the clinical workflow and reduces uncertainties. In the current paper we use a conditional Generative Adversarial Network (cGAN) framework called pix2pixHD to create a robust model prone to multicenter data.This study included T2-weighted MR and CT images of 19 patients in treatment position from 3 different sites. The cGAN was trained on 2D transverse slices of 11 patients from 2 different sites. Once trained, the network was used to generate sCT images of 8 patients coming from a third site. The Mean Absolute Errors (MAE) for each patient were evaluated between real and synthetic CTs. A radiotherapy plan was optimized on the sCT series and re-calculated on CTs to assess the dose distribution in terms of voxel-wise dose difference and Dose Volume Histograms (DVH) analysis.It takes on average of to generate a complete sCT (88 slices) for a patient on our GPU. The average MAE in HU between the sCT and actual patient CT (within the body contour) is 48.5 ± 6 HU with our method. The maximum dose difference to the target is 1.3%.This study demonstrates that an sCT can be generated in a multicentric context, with fewer pre-processing steps while being fast and accurate.Lire moins >
Lire la suite >The establishment of an MRI-only workflow in radiotherapy depends on the ability to generate an accurate synthetic CT (sCT) for dose calculation. Previously proposed methods have used a Generative Adversarial Network (GAN) for fast sCT generation in order to simplify the clinical workflow and reduces uncertainties. In the current paper we use a conditional Generative Adversarial Network (cGAN) framework called pix2pixHD to create a robust model prone to multicenter data.This study included T2-weighted MR and CT images of 19 patients in treatment position from 3 different sites. The cGAN was trained on 2D transverse slices of 11 patients from 2 different sites. Once trained, the network was used to generate sCT images of 8 patients coming from a third site. The Mean Absolute Errors (MAE) for each patient were evaluated between real and synthetic CTs. A radiotherapy plan was optimized on the sCT series and re-calculated on CTs to assess the dose distribution in terms of voxel-wise dose difference and Dose Volume Histograms (DVH) analysis.It takes on average of to generate a complete sCT (88 slices) for a patient on our GPU. The average MAE in HU between the sCT and actual patient CT (within the body contour) is 48.5 ± 6 HU with our method. The maximum dose difference to the target is 1.3%.This study demonstrates that an sCT can be generated in a multicentric context, with fewer pre-processing steps while being fast and accurate.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
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