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Showing results for qin an gan
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MR-based synthetic CT image for intensity-modulated proton treatment planning of nasopharyngeal carcinoma patients.
Chen S, Peng Y, Qin A, Liu Y, Zhao C, Deng X, Deraniyagala R, Stevens C, Ding X. Chen S, et al. Among authors: qin a. Acta Oncol. 2022 Nov;61(11):1417-1424. doi: 10.1080/0284186X.2022.2140017. Epub 2022 Oct 28. Acta Oncol. 2022. PMID: 36305424
RESULTS: The mean absolute errors (MAEs) between the PCT and SCT, within the body, were (64.89 5.31) HU and (64.31 4.61) HU for the GAN(2D) and GAN(3D). Within the high-density bone (HU > 600), the GAN(3D) achieved a smaller MAE compared with the GAN …
RESULTS: The mean absolute errors (MAEs) between the PCT and SCT, within the body, were (64.89 5.31) HU and (64.31 4.61) HU for the GAN …
Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning.
Peng Y, Chen S, Qin A, Chen M, Gao X, Liu Y, Miao J, Gu H, Zhao C, Deng X, Qi Z. Peng Y, et al. Among authors: qin a. Radiother Oncol. 2020 Sep;150:217-224. doi: 10.1016/j.radonc.2020.06.049. Epub 2020 Jul 3. Radiother Oncol. 2020. PMID: 32622781 Free article.
The MR and CT images of 28 patients were randomly chosen as the independent tested set. The remaining images were used to build a conditional GAN (cGAN) and a cycle-consistency GAN (cycleGAN). A U-net was used as the generator in cGAN, whereas a residual-Unet was us …
The MR and CT images of 28 patients were randomly chosen as the independent tested set. The remaining images were used to build a conditiona …