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MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis.
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Bahrami, K., Shi, F., Rekik, I., Shen, D.: Convolutional neural network for reconstruction of 7T-like images from 3T MRI using appearance and anatomical features. In: International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. pp. 39–47. Springer (2016)
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Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision. pp. 694–711. Springer (2016)
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Nie, D., Cao, X., Gao, Y., Wang, L., Shen, D.: Estimating ct image from mri data using 3d fully convolutional networks. In: Carneiro, G., Mateus, D., Peter, L., Bradley, A., Tavares, J.M.R.S., Belagiannis, V., Papa, J.P., Nascimento, J.C., Loog, M., Lu, Z., Cardoso, J.S., Cornebise, J. (eds.) Deep Learning and Data Labeling for Medical Applications: First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings. pp. 170–178. Springer International Publishing, Cham (2016), http://dx.doi.org/10.1007/978-3-319-46976-8_18
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Han, X.: MR-based synthetic CT generation using a deep convolutional neural network method. Medical Physics 44(4), 1408–1419 (2017)
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Wolterink, J.M., Leiner, T., Viergever, M.A., Isgum, I.: Generative adversarial networks for noise reduction in low-dose CT. IEEE Transactions on Medical Imaging (2017)
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Edmund, J.M., Nyholm, T.: A review of substitute CT generation for MRI-only radiation therapy. Radiation Oncology 12(1), 28 (2017), http://dx.doi.org/10.1186/s13014-016-0747-y
2017
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