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In this work we present a novel system for generation of virtual PET images using CT scans.
Adelson, E. H., Anderson, C. H., Bergen, J. R., Burt, P. J., and Ogden, J. M.,1984. Pyramid methods in image processing. RCA engineer, 29(6), pp. 33-41
1984
Earlier work this paper cites.
Higashi, K., Clavo, A. C., and Wahl, R. L.,1993. Does FDG Uptake Measure the Proliferative Activity of Human Cancer Cells? In Vitro Comparison with DNA Flow Cytometry and Tritiated Thymidine Uptake. Journal of Nuclear Medicine, 34, 414-414
1993
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P.,1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324
1998
Earlier work this paper cites.
Kostakoglu, L., Agress Jr, H., and Goldsmith, S. J., 2003. Clinical role of FDG PET in evaluation of cancer patients. Radiographics, 23(2), 315-340
2003
Earlier work this paper cites.
Kelloff, G. J., Hoffman, J. M., Johnson, B., Scher, H. I., Siegel, B. A., Cheng, E. Y., and Shankar, L., 2005. Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development. Clinical Cancer Research, 11(8), 2785-2808
2005
Earlier work this paper cites.
Metz, C. E., 2006. Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. Journal of the American College of Radiology, 3(6), 413-422
2006
Earlier work this paper cites.
Weber, W. A., Grosu, A. L., and Czernin, J., 2008. Technology Insight: advances in molecular imaging and an appraisal of PET/CT scanning. Nature Clinical Practice Oncology, 5(3), 160-170
2008
Earlier work this paper cites.
Weber, W. A., 2009. Assessing tumor response to therapy. Journal of nuclear medicine, 50(Suppl 1), 1S-10S
2009
Earlier work this paper cites.
Kinehan, P. E., and Fletcher, J. W., 2010. PET/CT standardized uptake values (SUVs) in clinical practice and assessing response to therapy. Semin Ultrasound CT MR, 31(6), 496-505
2010
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., and Bengio, Y.,2014. Generative adversarial nets. In Advances in neural information processing systems, pp. 2672-2680
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Chollet, François et al.: Keras. https://github.com/keras-team/keras . GitHub, (2015)
2015
Cited alongside, same era.
Ronneberger, O., Fischer, P., and Brox, T.,2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer International Publishing, pp. 234-241
Ben-Cohen, A., Klang, E., Kerpel, A., Konen, E., Amitai, M. M., and Greenspan, H., 2017. Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations. Neurocomputing
2017
Later among the works it cites.
Ben-Cohen, A., Klang, E., Raskin, S. P., Amitai, M. M., and Greenspan, H.,2017. Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results. In International Workshop on Simulation and Synthesis in Medical Imaging. Springer, Cham, pp. 49-57
2017
Later among the works it cites.
Bi, L., Kim, J., Kumar, A., Feng, D., and Fulham, M., 2017. Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs). In Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. Springer, Cham, pp. 43-51
2017
Later among the works it cites.
Chartsias, A., Joyce, T., Dharmakumar, R., and Tsaftaris, S. A., 2017. Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data. In International Workshop on Simulation and Synthesis in Medical Imaging. Springer, Cham, pp. 3-13
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2015
Cited alongside, same era.
Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., and Greenspan, H., 2016. Fully Convolutional Network for Liver Segmentation and Lesions Detection. In International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Springer International Publishing, pp. 77-85
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Nie, D., Cao, X., Gao, Y., Wang, L., and Shen, D., 2016. Estimating CT image from MRI data using 3D fully convolutional networks. In International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Springer International Publishing, pp. 170-178
2016
Cited alongside, same era.
Shelhamer, E., Long, J., and Darrell, T., 2016. Fully convolutional networks for semantic segmentation. IEEE transactions on pattern analysis and machine intelligence
2016
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
Han, X.,2017. MR based synthetic CT generation using a deep convolutional neural network method. Medical Physics, 44(4), pp. 1408-1419
2017
Later among the works it cites.
Wolterink, J. M., Dinkla, A. M., Savenije, M. H., Seevinck, P. R., van den Berg, C. A., and Išgum, I., 2017. Deep MR to CT synthesis using unpaired data. In International Workshop on Simulation and Synthesis in Medical Imaging. Springer, Cham, pp. 14-23
2017
Later among the works it cites.