seungryong cho
seungryong cho
Professor of Nuclear and Quantum Engineering, KAIST
Verified email at
Cited by
Cited by
Imaging system performing substantially exact reconstruction and using non-traditional trajectories
X Pan, Y Zou, L Yu, C Kao, M King, M Giger, D Xia, H Halpern, C Pelizzari, ...
US Patent 7,444,011, 2008
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
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
Energy-efficient probabilistic routing algorithm for internet of things
SH Park, S Cho, JR Lee
Journal of Applied Mathematics 2014, 2014
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
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
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
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
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
View-interpolation of sparsely sampled sinogram using convolutional neural network
H Lee, J Lee, S Cho
Medical Imaging 2017: Image Processing 10133, 1013328, 2017
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, 2012
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
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
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
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
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
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
Imaging system
X Pan, Y Zou, L Yu, C Kao, M King, M Giger, D Xia, H Halpern, C Pelizzari, ...
US Patent 8,121,245, 2012
A BPF‐FBP tandem algorithm for image reconstruction in reverse helical cone‐beam CT
S Cho, D Xia, CA Pellizzari, X Pan
Medical physics 37 (1), 32-39, 2010
Evaluation of radiation dose to organs during kilovoltage cone‐beam computed tomography using Monte Carlo simulation
K Son, S Cho, JS Kim, Y Han, SG Ju, DH Choi
Journal of applied clinical medical physics 15 (2), 295-302, 2014
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