MR to CT synthesis with multicenter data in the pelvic area using a conditional generative adversarial network
| Autor: | A. Wagner, John Klein, David Pasquier, Kévin Brou Boni, L. Vanquin, Thomas Lacornerie, Nick Reynaert |
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| Přispěvatelé: | Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre Régional de Lutte contre le Cancer Oscar Lambret [Lille] (UNICANCER/Lille), Université Lille Nord de France (COMUE)-UNICANCER, Université de Lille-UNICANCER |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
Computer science medicine.medical_treatment Context (language use) Image processing Pelvis 030218 nuclear medicine & medical imaging 03 medical and health sciences [SPI]Engineering Sciences [physics] 0302 clinical medicine Histogram Image Processing Computer-Assisted medicine Humans Multicenter Studies as Topic Radiology Nuclear Medicine and imaging ComputingMilieux_MISCELLANEOUS Radiological and Ultrasound Technology Rectal Neoplasms business.industry Radiotherapy Planning Computer-Assisted Prostatic Neoplasms Radiotherapy Dosage Pattern recognition Magnetic Resonance Imaging Radiation therapy 030220 oncology & carcinogenesis Maximum dose Radiotherapy Intensity-Modulated Artificial intelligence Tomography Tomography X-Ray Computed business Generative adversarial network Algorithms [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis Statistics and Probability [physics.data-an] |
| Zdroj: | Physics in Medicine and Biology Physics in Medicine and Biology, 2020, 65 (7), pp.075002. ⟨10.1088/1361-6560/ab7633⟩ Physics in Medicine and Biology, IOP Publishing, 2020, 65 (7), pp.075002. ⟨10.1088/1361-6560/ab7633⟩ |
| ISSN: | 0031-9155 1361-6560 |
| Popis: | 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 [Formula: see text] 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. |
| Databáze: | OpenAIRE |
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