| Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction H Lee, J Lee, H Kim, B Cho, S Cho IEEE Transactions on Radiation and Plasma Medical Sciences 3 (2), 109-119, 2018 | 177 | 2018 |
| Imaging system performing substantially exact reconstruction and using non-traditional trajectories X Pan, Y Zou, L Yu, CM Kao, M King, M Giger, D Xia, H Halpern, ... US Patent 7,444,011, 2008 | 111 | 2008 |
| Endodontic treatment of an anomalous anterior tooth with the aid of a 3-dimensional printed physical tooth model C Byun, C Kim, S Cho, SH Baek, G Kim, SG Kim, SY Kim Journal of endodontics 41 (6), 961-965, 2015 | 93 | 2015 |
| Deep learning diffuse optical tomography J Yoo, S Sabir, D Heo, KH Kim, A Wahab, Y Choi, SI Lee, EY Chae, ... IEEE transactions on medical imaging 39 (4), 877-887, 2019 | 78 | 2019 |
| Effects of sparse sampling schemes on image quality in low‐dose CT S Abbas, T Lee, S Shin, R Lee, S Cho Medical physics 40 (11), 111915, 2013 | 68 | 2013 |
| Energy-efficient probabilistic routing algorithm for internet of things SH Park, S Cho, JR Lee Journal of Applied Mathematics 2014, 2014 | 66 | 2014 |
| View-interpolation of sparsely sampled sinogram using convolutional neural network H Lee, J Lee, S Cho Medical Imaging 2017: Image Processing 10133, 617-624, 2017 | 59 | 2017 |
| Imaging system X Pan, Y Zou, L Yu, CM Kao, M King, M Giger, D Xia, H Halpern, ... US Patent App. 12/288,480, 2009 | 49 | 2009 |
| Region‐of‐interest image reconstruction with intensity weighting in circular cone‐beam CT for image‐guided radiation therapy S Cho, E Pearson, CA Pelizzari, X Pan Medical physics 36 (4), 1184-1192, 2009 | 47 | 2009 |
| Region‐of‐interest image reconstruction in circular cone‐beam microCT S Cho, J Bian, CA Pelizzari, CT Chen, TC He, X Pan Medical physics 34 (12), 4923-4933, 2007 | 39 | 2007 |
| Feasibility study on many-view under-sampling technique for low-dose computed tomography S Cho, T Lee, J Min, H Chung Optical Engineering 51 (8), 080501-080501, 2012 | 38 | 2012 |
| Fluence-map generation for prostate intensity-modulated radiotherapy planning using a deep-neural-network H Lee, H Kim, J Kwak, YS Kim, SW Lee, S Cho, B Cho Scientific reports 9 (1), 1-11, 2019 | 37 | 2019 |
| A feasibility study of low-dose single-scan dual-energy cone-beam CT in many-view under-sampling framework D Lee, J Lee, H Kim, T Lee, J Soh, M Park, C Kim, YJ Lee, S Cho IEEE transactions on medical imaging 36 (12), 2578-2587, 2017 | 33 | 2017 |
| Super-sparsely view-sampled cone-beam CT by incorporating prior data S Abbas, J Min, S Cho Journal of X-ray science and technology 21 (1), 71-83, 2013 | 32 | 2013 |
| Exact reconstruction of volumetric images in reverse helical cone‐beam CT S Cho, D Xia, CA Pelizzari, X Pan Medical physics 35 (7Part1), 3030-3040, 2008 | 30 | 2008 |
| Suppression of avalanche multiplication at the periphery of diffused junction by floating guard rings in a planar InGaAs-InP avalanche photodiode SR Cho, SK Yang, JS Ma, SD Lee, JS Yu, AG Choo, TI Kim, J Burm IEEE Photonics Technology Letters 12 (5), 534-536, 2000 | 30 | 2000 |
| Data consistency-driven scatter kernel optimization for x-ray cone-beam CT C Kim, M Park, Y Sung, J Lee, J Choi, S Cho Physics in Medicine & Biology 60 (15), 5971, 2015 | 27 | 2015 |
| Fully iterative scatter corrected digital breast tomosynthesis using GPU‐based fast Monte Carlo simulation and composition ratio update K Kim, T Lee, Y Seong, J Lee, KE Jang, J Choi, YW Choi, HH Kim, ... Medical physics 42 (9), 5342-5355, 2015 | 25 | 2015 |
| Enhanced optical coupling performance in an InGaAs photodiode integrated with wet-etched microlens SR Cho, J Kim, KS Oh, SK Yang, JM Baek, DH Jang, TI Kim, H Jeon IEEE Photonics Technology Letters 14 (3), 378-380, 2002 | 25 | 2002 |
| Imaging system X Pan, Y Zou, L Yu, CM Kao, M King, M Giger, D Xia, H Halpern, ... US Patent 8,121,245, 2012 | 24 | 2012 |
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