참고문헌 (24)

1. Jung, S.C., Yeom, J.A., Kim, J.-H., Ryoo, I., Kim, S.C., Shin, H., Lee, A.L., Yun, T.J., Park, C.-K., Sohn, C.-H., Park, S.-H., Choi, S.H.. Glioma: Application of Histogram Analysis of Pharmacokinetic Parameters from T1-Weighted Dynamic Contrast-Enhanced MR Imaging to Tumor Grading. AJNR, American journal of neuroradiology, vol.35, no.6, 1103-1110.

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2. Yun, Tae Jin, Park, Chul-Kee, Kim, Tae Min, Lee, Se-Hoon, Kim, Ji-Hoon, Sohn, Chul-Ho, Park, Sung-Hye, Kim, Il Han, Choi, Seung Hong. Glioblastoma Treated with Concurrent Radiation Therapy and Temozolomide Chemotherapy: Differentiation of True Progression from Pseudoprogression with Quantitative Dynamic Contrast-enhanced MR Imaging. Radiology, vol.274, no.3, 830-840.

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3. Sourbron, Steven P., Buckley, David L.. On the scope and interpretation of the Tofts models for DCE‐MRI. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine, vol.66, no.3, 735-745.

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4. Heye, Tobias, Davenport, Matthew S., Horvath, Jeffrey J., Feuerlein, Sebastian, Breault, Steven R., Bashir, Mustafa R., Merkle, Elmar M., Boll, Daniel T.. Reproducibility of Dynamic Contrast-enhanced MR Imaging. Part I. Perfusion Characteristics in the Female Pelvis by Using Multiple Computer-aided Diagnosis Perfusion Analysis Solutions. Radiology, vol.266, no.3, 801-811.

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5. Rata, Mihaela, Collins, David J., Darcy, James, Messiou, Christina, Tunariu, Nina, Desouza, Nandita, Young, Helen, Leach, Martin O., Orton, Matthew R.. Assessment of repeatability and treatment response in early phase clinical trials using DCE-MRI: comparison of parametric analysis using MR- and CT-derived arterial input functions. European radiology, vol.26, 1991-1998.

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6. Port, Ruediger E., Knopp, Michael V., Brix, Gunnar. Dynamic contrast-enhanced MRI using Gd-DTPA: Interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine, vol.45, no.6, 1030-1038.

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7. Sourbron, S.. Technical aspects of MR perfusion. European journal of radiology, vol.76, no.3, 304-313.

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8. Bae, Sohi, Choi, Yoon Seong, Ahn, Sung Soo, Chang, Jong Hee, Kang, Seok-Gu, Kim, Eui Hyun, Kim, Se Hoon, Lee, Seung-Koo. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. Radiology, vol.289, no.3, 797-806.

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9. Kim, Jung Youn, Park, Ji Eun, Jo, Youngheun, Shim, Woo Hyun, Nam, Soo Jung, Kim, Jeong Hoon, Yoo, Roh-Eul, Choi, Seung Hong, Kim, Ho Sung. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro-oncology, vol.21, no.3, 404-414.

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10. You, Sung-Hye, Choi, Seung Hong, Kim, Tae Min, Park, Chul-Kee, Park, Sung-Hye, Won, Jae-Kyung, Kim, Il Han, Lee, Soon Tae, Choi, Hye Jeong, Yoo, Roh-Eul, Kang, Koung Mi, Yun, Tae Jin, Kim, Ji-Hoon, Sohn, Chul-Ho. Differentiation of High-Grade from Low-Grade Astrocytoma: Improvement in Diagnostic Accuracy and Reliability of Pharmacokinetic Parameters from DCE MR Imaging by Using Arterial Input Functions Obtained from DSC MR Imaging. Radiology, vol.286, no.3, 981-991.

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11. McDonald, Robert J., McDonald, Jennifer S., Kallmes, David F., Jentoft, Mark E., Murray, David L., Thielen, Kent R., Williamson, Eric E., Eckel, Laurence J.. Intracranial Gadolinium Deposition after Contrast-enhanced MR Imaging. Radiology, vol.275, no.3, 772-782.

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12. Advances in Neural Information Processing Systems Goodfellow I 27 2014 

13. 10.1007/978-3-319-66179-7_48 

    

14. Wolterink, Jelmer M., Leiner, Tim, Viergever, Max A., Isgum, Ivana. Generative Adversarial Networks for Noise Reduction in Low-Dose CT. IEEE transactions on medical imaging, vol.36, no.12, 2536-2545.

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15. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Isola P 1125 2017 

16. Advances in Neural Information Processing Systems Gulrajani I 30 2017 

17. Bishop, Chris M.. Training with Noise is Equivalent to Tikhonov Regularization. Neural computation, vol.7, no.1, 108-116.

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18. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Zheng S 4480 2016 

19. Calamante, F.. Arterial input function in perfusion MRI: A comprehensive review. Progress in nuclear magnetic resonance spectroscopy, vol.74, 1-32.

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20. Yang, L., Krefting, I., Gorovets, A., Marzeila, L., Kaiser, J., Boucher, R., Rieves, D.. Nephrogenic Systemic Fibrosis and Class Labeling of Gadolinium-based Contrast Agents by the Food and Drug Administration. Radiology, vol.265, no.1, 248-253.

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21. Aerts, Hugo J. W. L., Velazquez, Emmanuel Rios, Leijenaar, Ralph T. H., Parmar, Chintan, Grossmann, Patrick, Cavalho, Sara, Bussink, Johan, Monshouwer, René, Haibe-Kains, Benjamin, Rietveld, Derek, Hoebers, Frank, Rietbergen, Michelle M., Leemans, C. René, Dekker, Andre, Quackenbush, John, Gillies, Robert J., Lambin, Philippe. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications, vol.5, 4006-.

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22. Choe, Jooae, Lee, Sang Min, Do, Kyung-Hyun, Lee, Gaeun, Lee, June-Goo, Lee, Sang Min, Seo, Joon Beom. Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses. Radiology, vol.292, no.2, 365-373.

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23. Choi, Kyu Sung, Choi, Seung Hong, Jeong, Bumseok. Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network. Neuro-oncology, vol.21, no.9, 1197-1209.

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24. Zhou, Zhi-Hua. A brief introduction to weakly supervised learning. National science review, vol.5, no.1, 44-53.

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