Kyungsang Kim

2.6k total citations
55 papers, 1.3k citations indexed

About

Kyungsang Kim is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Radiation. According to data from OpenAlex, Kyungsang Kim has authored 55 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Radiology, Nuclear Medicine and Imaging, 25 papers in Biomedical Engineering and 10 papers in Radiation. Recurrent topics in Kyungsang Kim's work include Medical Imaging Techniques and Applications (47 papers), Advanced X-ray and CT Imaging (24 papers) and Advanced MRI Techniques and Applications (22 papers). Kyungsang Kim is often cited by papers focused on Medical Imaging Techniques and Applications (47 papers), Advanced X-ray and CT Imaging (24 papers) and Advanced MRI Techniques and Applications (22 papers). Kyungsang Kim collaborates with scholars based in United States, South Korea and China. Kyungsang Kim's co-authors include Quanzheng Li, Georges El Fakhri, Dufan Wu, Kuang Gong, Jaewon Yang, Youngho Seo, Jianan Cui, Jong Chul Ye, Huafeng Liu and Joyita Dutta and has published in prestigious journals such as Scientific Reports, The British Journal of Psychiatry and IEEE Access.

In The Last Decade

Kyungsang Kim

49 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kyungsang Kim United States 17 1.1k 632 215 210 78 55 1.3k
Zhaoying Bian China 19 1.3k 1.1× 1.0k 1.6× 265 1.2× 155 0.7× 76 1.0× 105 1.5k
Yuxiang Xing China 20 865 0.8× 783 1.2× 122 0.6× 188 0.9× 88 1.1× 129 1.1k
Kuang Gong United States 20 1.5k 1.3× 580 0.9× 363 1.7× 432 2.1× 79 1.0× 71 1.8k
Wenxiang Cong United States 17 761 0.7× 654 1.0× 169 0.8× 82 0.4× 46 0.6× 53 965
Dufan Wu United States 16 712 0.6× 463 0.7× 166 0.8× 102 0.5× 62 0.8× 59 901
Jean‐Baptiste Thibault United States 16 1.5k 1.3× 1.3k 2.0× 121 0.6× 165 0.8× 111 1.4× 36 1.6k
Yoseob Han South Korea 11 766 0.7× 490 0.8× 250 1.2× 93 0.4× 25 0.3× 24 1.0k
Abolfazl Mehranian Switzerland 19 1.1k 0.9× 558 0.9× 79 0.4× 231 1.1× 38 0.5× 48 1.2k
Yongyi Shi United States 5 780 0.7× 566 0.9× 481 2.2× 80 0.4× 48 0.6× 22 1.1k

Countries citing papers authored by Kyungsang Kim

Since Specialization
Citations

This map shows the geographic impact of Kyungsang Kim's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kyungsang Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyungsang Kim more than expected).

Fields of papers citing papers by Kyungsang Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kyungsang Kim. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kyungsang Kim. The network helps show where Kyungsang Kim may publish in the future.

Co-authorship network of co-authors of Kyungsang Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Kyungsang Kim. A scholar is included among the top collaborators of Kyungsang Kim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kyungsang Kim. Kyungsang Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Jung, Young‐Chul, et al.. (2025). Promises and pitfalls of large language models in psychiatric diagnosis and knowledge tasks. The British Journal of Psychiatry. 226(4). 243–244.
2.
3.
Wu, Dufan, Boohwi Hong, Dongheon Lee, et al.. (2024). Texture-preserving low dose CT image denoising using Pearson divergence. Physics in Medicine and Biology. 69(11). 115021–115021. 3 indexed citations
4.
Kim, Kyungsang, Fabíola Macruz, Dufan Wu, et al.. (2023). Point-of-care AI-assisted stepwise ultrasound pneumothorax diagnosis. Physics in Medicine and Biology. 68(20). 205013–205013. 13 indexed citations
5.
Kim, Young-Gon, Kyungsang Kim, Dufan Wu, et al.. (2022). Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis. Diagnostics. 12(1). 101–101. 15 indexed citations
6.
Cui, Jianan, Kuang Gong, Ning Guo, et al.. (2022). Unsupervised PET logan parametric image estimation using conditional deep image prior. Medical Image Analysis. 80. 102519–102519. 11 indexed citations
7.
Ebrahimian, Shadi, Fatemeh Homayounieh, Marcio Aloísio Bezerra Cavalcanti Rockenbach, et al.. (2021). Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study. Scientific Reports. 11(1). 858–858. 30 indexed citations
8.
Xu, Pengcheng, Kyungsang Kim, Dufan Wu, et al.. (2021). Efficient knowledge distillation for liver CT segmentation using growing assistant network. Physics in Medicine and Biology. 66(23). 235005–235005. 7 indexed citations
9.
Park, Hyoung Suk, Kyungsang Kim, & Kiwan Jeon. (2020). Low-Dose CT Image Reconstruction With a Deep Learning Prior. IEEE Access. 8. 158647–158655. 6 indexed citations
10.
Kim, Kyungsang, Kuang Gong, Sung‐Hyun Moon, et al.. (2020). Penalized Parametric PET Image Estimation Using Local Linear Fitting. IEEE Transactions on Radiation and Plasma Medical Sciences. 4(6). 750–758.
11.
Kim, Kyungsang, Mengdie Wang, Ning Guo, Joshua Schaefferkoetter, & Quanzheng Li. (2020). Data-driven respiratory gating based on localized diaphragm sensing in TOF PET. Physics in Medicine and Biology. 65(16). 165007–165007. 3 indexed citations
12.
Song, Tzu-An, Fan Yang, Samadrita Roy Chowdhury, et al.. (2019). PET Image Deblurring and Super-Resolution With an MR-Based Joint Entropy Prior. IEEE Transactions on Computational Imaging. 5(4). 530–539. 26 indexed citations
13.
Cui, Jianan, Kuang Gong, Ning Guo, et al.. (2019). PET image denoising using unsupervised deep learning. European Journal of Nuclear Medicine and Molecular Imaging. 46(13). 2780–2789. 181 indexed citations
14.
Gong, Kuang, Dufan Wu, Kyungsang Kim, et al.. (2019). MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction. 102–102. 28 indexed citations
15.
Gong, Kuang, Jiahui Guan, Kyungsang Kim, et al.. (2018). Iterative PET Image Reconstruction Using Convolutional Neural Network Representation. IEEE Transactions on Medical Imaging. 38(3). 675–685. 174 indexed citations
16.
Kim, Kyungsang, Dufan Wu, Kuang Gong, et al.. (2018). Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting. IEEE Transactions on Medical Imaging. 37(6). 1478–1487. 137 indexed citations
17.
Gong, Kuang, Jaewon Yang, Kyungsang Kim, et al.. (2018). Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images. Physics in Medicine and Biology. 63(12). 125011–125011. 84 indexed citations
18.
Kim, Kyungsang, et al.. (2018). A novel depth-of-interaction rebinning strategy for ultrahigh resolution PET. Physics in Medicine and Biology. 63(16). 165011–165011. 9 indexed citations
19.
Wu, Dufan, Kyungsang Kim, Georges El Fakhri, & Quanzheng Li. (2017). Iterative Low-Dose CT Reconstruction With Priors Trained by Artificial Neural Network. IEEE Transactions on Medical Imaging. 36(12). 2479–2486. 178 indexed citations
20.
Kim, Kyungsang, Jong Chul Ye, W. Worstell, et al.. (2015). Sparse-View Spectral CT Reconstruction Using Spectral Patch-Based Low-Rank Penalty. IEEE Transactions on Medical Imaging. 34(3). 748–760. 122 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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