참고문헌 (37)

1. Science AP Alivisatos 271 933 1996 10.1126/science.271.5251.933 Alivisatos, A. P. Semiconductor clusters, nanocrystals, and quantum dots. Science 271, 933-937 (1996). 

   상세보기

2. Phys. Rev. A D Loss 57 120 1998 10.1103/PhysRevA.57.120 Loss, D. & DiVincenzo, D. P. Quantum computation with quantum dots. Phys. Rev. A 57, 120 (1998). 

   상세보기

3. Nature Y-H Won 575 634 2019 10.1038/s41586-019-1771-5 Won, Y.-H. et al. Highly efficient and stable inp/znse/zns quantum dot light-emitting diodes. Nature 575, 634-638 (2019). 

   상세보기

4. 10.1007/978-1-4939-6676-9_27 Goldstein, J. I. et al. Scanning Electron Microscopy and X-ray Microanalysis (Springer, 2017). 

   

5. Micron K Lepinay 47 43 2013 10.1016/j.micron.2013.01.004 Lepinay, K., Lorut, F., Pantel, R. & Epicier, T. Chemical 3D tomography of 28 nm high K metal gate transistor: STEM XEDS experimental method and results. Micron 47, 43-49 (2013). 

   상세보기

6. Ultramicroscopy TJ Slater 162 61 2016 10.1016/j.ultramic.2015.10.007 Slater, T. J. et al. STEM-EDX tomography of bimetallic nanoparticles: a methodological investigation. Ultramicroscopy 162, 61-73 (2016). 

   상세보기

7. Appl. Phys. Lett. Z Saghi 91 251906 2007 10.1063/1.2826273 Saghi, Z., Xu, X., Peng, Y., Inkson, B. & Möbus, G. Three-dimensional chemical analysis of tungsten probes by energy dispersive x-ray nanotomography. Appl. Phys. Lett. 91, 251906 (2007). 

   상세보기

8. Ultramicroscopy Z Zhong 174 35 2017 10.1016/j.ultramic.2016.12.008 Zhong, Z., Goris, B., Schoenmakers, R., Bals, S. & Batenburg, K. J. A bimodal tomographic reconstruction technique combining EDS-STEM and HAADF-STEM. Ultramicroscopy 174, 35-45 (2017). 

   상세보기

9. J. Theor. Biol. P Gilbert 36 105 1972 10.1016/0022-5193(72)90180-4 Gilbert, P. Iterative methods for the three-dimensional reconstruction of an object from projections. J. Theor. Biol. 36, 105-117 (1972). 

   상세보기

10. 10.1002/mp.12344 Kang, E., Min, J. & Ye, J. C. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med. Phys. 44, e360-e375 (2017). 

    상세보기

11. IEEE Trans. Med. Imaging H Chen 36 2524 2017 10.1109/TMI.2017.2715284 Chen, H. et al. Low-dose CT with a residual encoder-decoder convolutional neural network. IEEE Trans. Med. Imaging 36, 2524-2535 (2017). 

    상세보기

12. 10.1109/TMI.2018.2823756 Kang, E., Chang, W., Yoo, J. & Ye, J. C. Deep convolutional framelet denosing for low-dose CT via wavelet residual network. IEEE Trans. Med. Imaging 37, 1358-1369 (2018). 

    상세보기

13. IEEE Trans. Image Process. KH Jin 26 4509 2017 10.1109/TIP.2017.2713099 Jin, K. H., McCann, M. T., Froustey, E. & Unser, M. Deep convolutional neural network for inverse problems in imaging. IEEE Trans. Image Process. 26, 4509-4522 (2017). 

    상세보기

14. IEEE Trans. Med. Imaging Y Han 37 1418 2018 10.1109/TMI.2018.2823768 Han, Y. & Ye, J. C. Framing U-Net via deep convolutional framelets: application to sparse-view CT. IEEE Trans. Med. Imaging 37, 1418-1429 (2018). 

    상세보기

15. Han, Y., Kang, J. & Ye, J. C. Deep learning reconstruction for 9-view dual energy CT baggage scanner. In Proc. Fifth International Conference on Image Formation in X-Ray Computed Tomography 407-410 (CT Meeting, 2018). 

16. 10.1109/ISBI.2016.7493320 Wang, S. et al. Accelerating magnetic resonance imaging via deep learning. In 2016 IEEE 13th International Symposium on Biomedical Imaging 514-517 (IEEE, 2016).. 

    

17. Magn. Reson. Med. K Hammernik 79 3055 2018 10.1002/mrm.26977 Hammernik, K. et al. Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med. 79, 3055-3071 (2018). 

    상세보기

18. 10.1109/TBME.2018.2821699 Lee, D., Yoo, J., Tak, S. & Ye, J. Deep residual learning for accelerated MRI using magnitude and phase networks. IEEE Trans. Biomed. Eng. 65, 1985-1995 (2018). 

    상세보기

19. 10.1002/mrm.27106 Han, Y. S., Yoo, J. & Ye, J. C. Deep learning with domain adaptation for accelerated projection reconstruction MR. Magn. Reson. Med. 80, 1189-1205 (2018). 

    상세보기

20. 10.1109/TMI.2018.2869871 Gong, K. et al. Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans. Med. Imaging 38, 675-685 (2018). 

    상세보기

21. 10.1109/TMI.2018.2888491 Gong, K., Catana, C., Qi, J. & Li, Q. PET image reconstruction using deep image prior. IEEE Trans. Med. Imaging 38, 1655-1665 (2018). 

    상세보기

22. IEEE Trans. Med. Imaging AC Luchies 37 2010 2018 10.1109/TMI.2018.2809641 Luchies, A. C. & Byram, B. C. Deep neural networks for ultrasound beamforming. IEEE Trans. Med. Imaging 37, 2010-2021 (2018). 

    상세보기

23. IEEE Trans. Med. Imaging YH Yoon 38 325 2019 10.1109/TMI.2018.2864821 Yoon, Y. H., Khan, S., Huh, J. & Ye, J. C. Efficient B-mode ultrasound image reconstruction from sub-sampled RF data using deep learning. IEEE Trans. Med. Imaging 38, 325-336 (2019). 

    상세보기

24. Optica Y Rivenson 4 1437 2017 10.1364/OPTICA.4.001437 Rivenson, Y. et al. Deep learning microscopy. Optica 4, 1437-1443 (2017). 

    상세보기

25. Optica E Nehme 5 458 2018 10.1364/OPTICA.5.000458 Nehme, E., Weiss, L. E., Michaeli, T. & Shechtman, Y. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5, 458-464 (2018). 

    상세보기

26. Optica A Sinha 4 1117 2017 10.1364/OPTICA.4.001117 Sinha, A., Lee, J., Li, S. & Barbastathis, G. Lensless computational imaging through deep learning. Optica 4, 1117-1125 (2017). 

    상세보기

27. IEEE Trans. Inf. Theory DL Donoho 52 1289 2006 10.1109/TIT.2006.871582 Donoho, D. L. Compressed sensing. IEEE Trans. Inf. Theory 52, 1289-1306 (2006). 

    상세보기

28. SIAM J. Imag. Sci. JC Ye 11 991 2018 10.1137/17M1141771 Ye, J. C., Han, Y. & Cha, E. Deep convolutional framelets: a general deep learning framework for inverse problems. SIAM J. Imag. Sci. 11, 991-1048 (2018). 

    상세보기

29. Ye, J. C. & Sung, W. K. Understanding geometry of encoder-decoder CNNs. In Proc. 36th International Conference on Machine Learning (eds Chaudhuri, K. & Salakhutdinov, R.) 7064-7073 (PMLR, 2019).. 

30. ACS Appl. Nano Mater. Y Kim 2 1496 2019 10.1021/acsanm.8b02063 Kim, Y. et al. Bright and uniform green light emitting InP/ZnSe/ZnS quantum dots for wide color gamut displays. ACS Appl. Nano Mater. 2, 1496-1504 (2019). 

    상세보기

31. Polym. Adv. Technol. S Lee 30 749 2019 10.1002/pat.4513 Lee, S. & Lee, C. High-density quantum dots composites and its photolithographic patterning applications. Polym. Adv. Technol. 30, 749-754 (2019). 

    상세보기

32. J. Am. Chem. Soc. H Virieux 134 19701 2012 10.1021/ja307124m Virieux, H. et al. InP/ZnS nanocrystals: coupling NMR and XPS for fine surface and interface description. J. Am. Chem. Soc. 134, 19701-19708 (2012). 

    상세보기

33. Chem. Mater. JL Stein 30 6377 2018 10.1021/acs.chemmater.8b02590 Stein, J. L. et al. Probing surface defects of InP quantum dots using phosphorus Kα and Kβ X-ray emission spectroscopy. Chem. Mater. 30, 6377-6388 (2018). 

    상세보기

34. Chem. Mater. D Hahm 31 3476 2019 10.1021/acs.chemmater.9b00740 Hahm, D. et al. Design principle for bright, robust, and color-pure InP/ZnSexS1-x/ZnS heterostructures. Chem. Mater. 31, 3476-3484 (2019). 

    상세보기

35. Nano Lett. KR Reid 18 709 2018 10.1021/acs.nanolett.7b03703 Reid, K. R., McBride, J. R., Freymeyer, N. J., Thal, L. B. & Rosenthal, S. J. Chemical structure, ensemble and single-particle spectroscopy of thick-shell inp-znse quantum dots. Nano Lett. 18, 709-716 (2018). 

    상세보기

36. J. Microsc. W Saxton 127 127 1982 10.1111/j.1365-2818.1982.tb00405.x Saxton, W. & Baumeister, W. The correlation averaging of a regularly arranged bacterial cell envelope protein. J. Microsc. 127, 127-138 (1982). 

    상세보기

37. 10.1007/978-3-319-46723-8_49 Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In International Conference on Medical Image Computing and Computer-Assisted Intervention 424-432 (Springer, 2016). 

    

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