Lipeng Ning

2.0k total citations
60 papers, 865 citations indexed

About

Lipeng Ning is a scholar working on Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience and Computational Mechanics. According to data from OpenAlex, Lipeng Ning has authored 60 papers receiving a total of 865 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Radiology, Nuclear Medicine and Imaging, 13 papers in Cognitive Neuroscience and 7 papers in Computational Mechanics. Recurrent topics in Lipeng Ning's work include Advanced Neuroimaging Techniques and Applications (36 papers), Advanced MRI Techniques and Applications (28 papers) and Functional Brain Connectivity Studies (13 papers). Lipeng Ning is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (36 papers), Advanced MRI Techniques and Applications (28 papers) and Functional Brain Connectivity Studies (13 papers). Lipeng Ning collaborates with scholars based in United States, China and Sweden. Lipeng Ning's co-authors include Yogesh Rathi, Carl‐Fredrik Westin, Tryphon T. Georgiou, Nikos Makris, Martha E. Shenton, Joan A. Camprodon, Allen Tannenbaum, Borjan Gagoski, Lauren J. O’Donnell and Marek Kubicki and has published in prestigious journals such as The Journal of Chemical Physics, PLoS ONE and NeuroImage.

In The Last Decade

Lipeng Ning

52 papers receiving 858 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lipeng Ning United States 18 618 231 122 84 72 60 865
Yaniv Gur United States 13 911 1.5× 201 0.9× 134 1.1× 45 0.5× 89 1.2× 31 1.2k
Hongjian He China 18 767 1.2× 316 1.4× 65 0.5× 33 0.4× 40 0.6× 87 1.1k
Dirk H. J. Poot Netherlands 23 1.3k 2.0× 146 0.6× 133 1.1× 35 0.4× 242 3.4× 94 1.9k
Michaël Sdika France 16 517 0.8× 66 0.3× 40 0.3× 65 0.8× 26 0.4× 45 946
Keith Heberlein United States 16 961 1.6× 457 2.0× 69 0.6× 17 0.2× 43 0.6× 27 1.3k
Ivan I. Maximov Norway 20 650 1.1× 314 1.4× 126 1.0× 29 0.3× 67 0.9× 67 1.2k
Enrico Kaden United Kingdom 15 934 1.5× 137 0.6× 171 1.4× 17 0.2× 219 3.0× 26 1.0k
Ferenc A. Jolesz United States 12 554 0.9× 72 0.3× 28 0.2× 40 0.5× 28 0.4× 12 824
Cheng Guan Koay United States 22 1.4k 2.2× 174 0.8× 196 1.6× 13 0.2× 165 2.3× 37 1.6k
Berkin Bilgic̦ United States 33 3.1k 5.0× 483 2.1× 109 0.9× 58 0.7× 52 0.7× 135 3.6k

Countries citing papers authored by Lipeng Ning

Since Specialization
Citations

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

Fields of papers citing papers by Lipeng Ning

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lipeng Ning. 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 Lipeng Ning. The network helps show where Lipeng Ning may publish in the future.

Co-authorship network of co-authors of Lipeng Ning

This figure shows the co-authorship network connecting the top 25 collaborators of Lipeng Ning. A scholar is included among the top collaborators of Lipeng Ning 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 Lipeng Ning. Lipeng Ning 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.
2.
Ning, Lipeng, et al.. (2024). AutoRL X: Automated Reinforcement Learning on the Web. ACM Transactions on Interactive Intelligent Systems. 14(4). 1–30.
3.
Ning, Lipeng, et al.. (2024). Neural orientation distribution fields for estimation and uncertainty quantification in diffusion MRI. Medical Image Analysis. 93. 103105–103105. 6 indexed citations
4.
Park, Tae‐Young, et al.. (2024). A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation. Biomedical Engineering Letters. 14(3). 393–405. 5 indexed citations
5.
Zhang, Jiachi, Kai Wang, Xiaoming Duan, et al.. (2024). A Novel GBSM for LEO Satellite-Ground Communication Large-Scale Channels. IEEE Internet of Things Journal. 12(8). 11049–11063.
6.
Zhang, Fan, Yuqian Chen, Lipeng Ning, et al.. (2024). Assessment of the Depiction of Superficial White Matter Using Ultra‐High‐Resolution Diffusion MRI. Human Brain Mapping. 45(14). e70041–e70041.
7.
Ning, Lipeng, Congyu Liao, Borjan Gagoski, et al.. (2024). Reduced cross‐scanner variability using vendor‐agnostic sequences for single‐shell diffusion MRI. Magnetic Resonance in Medicine. 92(1). 246–256. 7 indexed citations
8.
Zhang, Fan, Kang Ik Kevin Cho, Johanna Seitz‐Holland, et al.. (2023). DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI. IEEE Transactions on Medical Imaging. 43(3). 1191–1202. 10 indexed citations
9.
Ji, Yang, W. Scott Hoge, Borjan Gagoski, et al.. (2022). Accelerating joint relaxation‐diffusion MRI by integrating time division multiplexing and simultaneous multi‐slice (TDM‐SMS) strategies. Magnetic Resonance in Medicine. 87(6). 2697–2709. 6 indexed citations
10.
Ji, Yang, Borjan Gagoski, W. Scott Hoge, Yogesh Rathi, & Lipeng Ning. (2021). Accelerated diffusion and relaxation‐diffusion MRI using time‐division multiplexing EPI. Magnetic Resonance in Medicine. 86(5). 2528–2541. 9 indexed citations
11.
Xu, Guoping, Yogesh Rathi, Joan A. Camprodon, Hanqiang Cao, & Lipeng Ning. (2021). Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning. PLoS ONE. 16(7). e0254588–e0254588. 20 indexed citations
12.
Ning, Lipeng, Borjan Gagoski, Filip Szczepankiewicz, Carl‐Fredrik Westin, & Yogesh Rathi. (2019). Joint RElaxation-Diffusion Imaging Moments to Probe Neurite Microstructure. IEEE Transactions on Medical Imaging. 39(3). 668–677. 26 indexed citations
13.
Ning, Lipeng. (2019). Smooth Interpolation of Covariance Matrices and Brain Network Estimation: Part II. IEEE Transactions on Automatic Control. 65(5). 1901–1910. 2 indexed citations
14.
Ning, Lipeng. (2018). Smooth Interpolation of Covariance Matrices and Brain Network Estimation. IEEE Transactions on Automatic Control. 64(8). 3184–3193. 3 indexed citations
15.
Karmacharya, Sarina, Borjan Gagoski, Lipeng Ning, et al.. (2018). Advanced diffusion imaging for assessing normal white matter development in neonates and characterizing aberrant development in congenital heart disease. NeuroImage Clinical. 19. 360–373. 35 indexed citations
16.
Ning, Lipeng, et al.. (2017). Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model. NeuroImage Clinical. 15. 819–831. 31 indexed citations
17.
Ning, Lipeng & Yogesh Rathi. (2017). A Dynamic Regression Approach for Frequency-Domain Partial Coherence and Causality Analysis of Functional Brain Networks. IEEE Transactions on Medical Imaging. 37(9). 1957–1969. 8 indexed citations
18.
Ning, Lipeng, Evren Özarslan, Carl‐Fredrik Westin, & Yogesh Rathi. (2016). Precise Inference and Characterization of Structural Organization (PICASO) of tissue from molecular diffusion. NeuroImage. 146. 452–473. 13 indexed citations
19.
Ning, Lipeng, Frederik B. Laun, Yaniv Gur, et al.. (2015). Sparse Reconstruction Challenge for diffusion MRI: Validation on a physical phantom to determine which acquisition scheme and analysis method to use?. Medical Image Analysis. 26(1). 316–331. 61 indexed citations
20.
Ning, Lipeng, et al.. (2014). Coping with model error in variational data assimilation using optimal mass transport. Water Resources Research. 50(7). 5817–5830. 17 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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