Sung‐Min Gho

677 total citations
26 papers, 506 citations indexed

About

Sung‐Min Gho is a scholar working on Radiology, Nuclear Medicine and Imaging, Atomic and Molecular Physics, and Optics and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Sung‐Min Gho has authored 26 papers receiving a total of 506 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Atomic and Molecular Physics, and Optics and 3 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in Sung‐Min Gho's work include Advanced MRI Techniques and Applications (18 papers), Advanced Neuroimaging Techniques and Applications (11 papers) and Atomic and Subatomic Physics Research (7 papers). Sung‐Min Gho is often cited by papers focused on Advanced MRI Techniques and Applications (18 papers), Advanced Neuroimaging Techniques and Applications (11 papers) and Atomic and Subatomic Physics Research (7 papers). Sung‐Min Gho collaborates with scholars based in South Korea, United States and Spain. Sung‐Min Gho's co-authors include Chunlei Liu, Wěi Li, Alexandru Avram, Bing Wu, Donghyun Kim, Eung Yeop Kim, Dong‐Hyun Kim, Yoonho Nam, Jongho Lee and Doo Kyoung Kang and has published in prestigious journals such as NeuroImage, Magnetic Resonance in Medicine and SLEEP.

In The Last Decade

Sung‐Min Gho

23 papers receiving 499 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sung‐Min Gho South Korea 12 383 100 52 47 42 26 506
Yingjian Yu United States 8 383 1.0× 104 1.0× 45 0.9× 30 0.6× 48 1.1× 16 604
Alexey Dimov United States 16 422 1.1× 136 1.4× 80 1.5× 19 0.4× 49 1.2× 36 691
Jianlin Wu China 7 540 1.4× 108 1.1× 84 1.6× 46 1.0× 66 1.6× 21 763
Sina Straub Germany 12 564 1.5× 106 1.1× 101 1.9× 20 0.4× 98 2.3× 33 777
Yunhong Shu United States 15 529 1.4× 63 0.6× 67 1.3× 14 0.3× 89 2.1× 61 625
Umesh Rudrapatna United Kingdom 13 464 1.2× 70 0.7× 91 1.8× 46 1.0× 27 0.6× 24 588
Martyn N.J. Paley United Kingdom 13 303 0.8× 119 1.2× 66 1.3× 92 2.0× 217 5.2× 33 667
Stephan Witoszynskyj Austria 9 372 1.0× 42 0.4× 34 0.7× 18 0.4× 72 1.7× 13 447
Arnaud Guidon United States 14 904 2.4× 81 0.8× 187 3.6× 48 1.0× 72 1.7× 30 1.0k
Carlos Milovic Chile 12 440 1.1× 64 0.6× 136 2.6× 12 0.3× 59 1.4× 27 577

Countries citing papers authored by Sung‐Min Gho

Since Specialization
Citations

This map shows the geographic impact of Sung‐Min Gho'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 Sung‐Min Gho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sung‐Min Gho more than expected).

Fields of papers citing papers by Sung‐Min Gho

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sung‐Min Gho. 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 Sung‐Min Gho. The network helps show where Sung‐Min Gho may publish in the future.

Co-authorship network of co-authors of Sung‐Min Gho

This figure shows the co-authorship network connecting the top 25 collaborators of Sung‐Min Gho. A scholar is included among the top collaborators of Sung‐Min Gho 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 Sung‐Min Gho. Sung‐Min Gho 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.
Al‐masni, Mohammed A., Seul Lee, Sun Young Jung, et al.. (2025). Unsupervised learning for motion correction and assessment in brain magnetic resonance imaging using severity-based regularized cycle consistency. Engineering Applications of Artificial Intelligence. 142. 109978–109978.
2.
Usman, Muhammad, et al.. (2024). Advancing Metaverse-Based Healthcare With Multimodal Neuroimaging Fusion via Multi-Task Adversarial Variational Autoencoder for Brain Age Estimation. IEEE Journal of Biomedical and Health Informatics. 30(3). 1867–1875.
3.
4.
Al‐masni, Mohammed A., et al.. (2023). A knowledge interaction learning for multi-echo MRI motion artifact correction towards better enhancement of SWI. Computers in Biology and Medicine. 153. 106553–106553. 5 indexed citations
5.
Kim, Tae‐Joon, Jung Hwan Kim, Jin‐Sun Jun, et al.. (2023). Change of iron content in brain regions after intravenous iron therapy in restless legs syndrome: quantitative susceptibility mapping study. SLEEP. 46(8). 9 indexed citations
6.
Cho, Eun, Hye Jin Baek, Filip Szczepankiewicz, et al.. (2022). Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer. Quantitative Imaging in Medicine and Surgery. 12(3). 2002–2017. 6 indexed citations
7.
Al‐masni, Mohammed A., et al.. (2022). Stacked U-Nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI. NeuroImage. 259. 119411–119411. 27 indexed citations
8.
Kim, Hyun Gi, Jin Wook Choi, Jang Hoon Lee, Da Eun Jung, & Sung‐Min Gho. (2021). Association of Cerebral Blood Flow and Brain Tissue Relaxation Time With Neurodevelopmental Outcomes of Preterm Neonates. Investigative Radiology. 57(4). 254–262. 8 indexed citations
9.
Ryu, Kanghyun, et al.. (2021). Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks. Medical Physics. 48(6). 2939–2950. 7 indexed citations
10.
Kim, Yeun‐Yoon, Myeong‐Jin Kim, Sung‐Min Gho, & Nieun Seo. (2020). Comparison of multiplexed sensitivity encoding and single-shot echo-planar imaging for diffusion-weighted imaging of the liver. European Journal of Radiology. 132. 109292–109292. 16 indexed citations
11.
Ryu, Kyeong Hwa, Hye Jin Baek, Sung‐Min Gho, et al.. (2020). Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment. Journal of Clinical Medicine. 9(2). 364–364. 3 indexed citations
12.
13.
Ryu, Kanghyun, Yoonho Nam, Sung‐Min Gho, et al.. (2019). Data‐driven synthetic MRI FLAIR artifact correction via deep neural network. Journal of Magnetic Resonance Imaging. 50(5). 1413–1423. 21 indexed citations
14.
Kim, Hyun Gi, et al.. (2018). Multidelay Arterial Spin-Labeling MRI in Neonates and Infants: Cerebral Perfusion Changes during Brain Maturation. American Journal of Neuroradiology. 39(10). 1912–1918. 20 indexed citations
15.
Ning, Ning, Congcong Liu, Peng Wu, et al.. (2018). Spatiotemporal variations of magnetic susceptibility in the deep gray matter nuclei from 1 month to 6 years: A quantitative susceptibility mapping study. Journal of Magnetic Resonance Imaging. 49(6). 1600–1609. 12 indexed citations
16.
Jung, Yong Sik, et al.. (2018). The feasibility of synthetic MRI in breast cancer patients: comparison of T 2 relaxation time with multiecho spin echo T 2 mapping method. British Journal of Radiology. 92(1093). 20180479–20180479. 45 indexed citations
17.
Park, Sunghoon, Kyu‐Sung Kwack, Young Ju Lee, Sung‐Min Gho, & Hyun Young Lee. (2017). Initial experience with synthetic MRI of the knee at 3T: comparison with conventional T1 weighted imaging and T2 mapping. British Journal of Radiology. 90(1080). 20170350–20170350. 15 indexed citations
18.
Kim, Dong‐Hyun, et al.. (2013). Simultaneous imaging of in vivo conductivity and susceptibility. Magnetic Resonance in Medicine. 71(3). 1144–1150. 36 indexed citations
19.
Gho, Sung‐Min, Chunlei Liu, Wěi Li, et al.. (2013). Susceptibility map‐weighted imaging (SMWI) for neuroimaging. Magnetic Resonance in Medicine. 72(2). 337–346. 39 indexed citations
20.
Wu, Bing, Wěi Li, Alexandru Avram, Sung‐Min Gho, & Chunlei Liu. (2011). Fast and tissue-optimized mapping of magnetic susceptibility and T2* with multi-echo and multi-shot spirals. NeuroImage. 59(1). 297–305. 138 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Yingjian Yu Breakdown of academic impact, for papers by Alexey Dimov Breakdown of academic impact, for papers by Jianlin Wu Breakdown of academic impact, for papers by Sina Straub Breakdown of academic impact, for papers by Yunhong Shu Breakdown of academic impact, for papers by Umesh Rudrapatna Breakdown of academic impact, for papers by Martyn N.J. Paley Breakdown of academic impact, for papers by Stephan Witoszynskyj Breakdown of academic impact, for papers by Arnaud Guidon Breakdown of academic impact, for papers by Carlos Milovic
Rankless by CCL
2026