Fan Lam

2.2k total citations
64 papers, 1.6k citations indexed

About

Fan Lam is a scholar working on Radiology, Nuclear Medicine and Imaging, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Fan Lam has authored 64 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Radiology, Nuclear Medicine and Imaging, 19 papers in Computational Mechanics and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in Fan Lam's work include Advanced MRI Techniques and Applications (43 papers), Sparse and Compressive Sensing Techniques (18 papers) and Medical Imaging Techniques and Applications (15 papers). Fan Lam is often cited by papers focused on Advanced MRI Techniques and Applications (43 papers), Sparse and Compressive Sensing Techniques (18 papers) and Medical Imaging Techniques and Applications (15 papers). Fan Lam collaborates with scholars based in United States, China and Hong Kong. Fan Lam's co-authors include Zhi‐Pei Liang, Chao Ma, Bo Zhao, Bryan Clifford, Yingkun Hou, Zhong Chen, Xiaobo Qu, Di Guo, Jianhui Zhong and Curtis L. Johnson and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Analytical Chemistry and Nature Methods.

In The Last Decade

Fan Lam

60 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fan Lam United States 22 1.3k 402 262 247 217 64 1.6k
Congbo Cai China 20 969 0.8× 164 0.4× 183 0.7× 250 1.0× 158 0.7× 103 1.3k
Jeffrey Tsao Switzerland 20 1.5k 1.2× 190 0.5× 133 0.5× 139 0.6× 360 1.7× 30 1.7k
Anthony Christodoulou United States 20 1.2k 0.9× 193 0.5× 76 0.3× 131 0.5× 169 0.8× 82 1.4k
Lawrence P. Panych United States 20 1.2k 0.9× 62 0.2× 205 0.8× 322 1.3× 280 1.3× 52 1.4k
Mariya Doneva Germany 17 924 0.7× 125 0.3× 71 0.3× 109 0.4× 176 0.8× 42 1.0k
Peter Shin United States 13 530 0.4× 102 0.3× 330 1.3× 100 0.4× 200 0.9× 28 780
Qi Duan China 19 310 0.2× 317 0.8× 108 0.4× 237 1.0× 97 0.4× 69 1.1k
Adam B. Kerr United States 22 1.4k 1.1× 82 0.2× 662 2.5× 143 0.6× 487 2.2× 69 1.7k
Jason Stockmann United States 21 1.5k 1.2× 37 0.1× 427 1.6× 218 0.9× 559 2.6× 75 1.8k
Ian C. Atkinson United States 13 499 0.4× 54 0.1× 184 0.7× 58 0.2× 129 0.6× 35 677

Countries citing papers authored by Fan Lam

Since Specialization
Citations

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

Fields of papers citing papers by Fan Lam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fan Lam

This figure shows the co-authorship network connecting the top 25 collaborators of Fan Lam. A scholar is included among the top collaborators of Fan Lam 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 Fan Lam. Fan Lam 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.
Cao, Chang, Jing Yuan, Elizabeth R. Gilbert, et al.. (2025). Increased Circulating Interleukin Concentrations in Type 2 Diabetes: A Systematic Review and Meta‐Analysis. Obesity Reviews. 26(12). e13971–e13971.
2.
3.
He, Jingfei, et al.. (2024). Denoising of 3D Magnetic resonance images based on balanced low-rank tensor and nonlocal self-similarity. Biomedical Signal Processing and Control. 96. 106588–106588. 1 indexed citations
4.
Ruhm, Loreen, Aaron Anderson, Paul M. Arnold, et al.. (2024). Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction. Magnetic Resonance in Medicine. 93(2). 455–469.
5.
Ruhm, Loreen, et al.. (2023). LeaRning nonlineAr representatIon and projectIon for faSt constrained MRSI rEconstruction (RAIISE). Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition. 2 indexed citations
6.
Lam, Fan, et al.. (2023). Generative Image Prior Constrained Subspace Reconstruction for High-Resolution MRSI. Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition. 2 indexed citations
7.
Lam, Fan, Ji Sun Choi, Chang Cao, et al.. (2022). Epigenetic MRI: Noninvasive imaging of DNA methylation in the brain. Proceedings of the National Academy of Sciences. 119(10). e2119891119–e2119891119. 6 indexed citations
8.
Naik, Anant, Aaron Anderson, Fan Lam, et al.. (2022). Ultra-High-Field MRI in the Diagnosis and Management of Gliomas: A Systematic Review. Frontiers in Neurology. 13. 857825–857825. 21 indexed citations
9.
Castro, Daniel C., et al.. (2022). Enhancing the Throughput of FT Mass Spectrometry Imaging Using Joint Compressed Sensing and Subspace Modeling. Analytical Chemistry. 94(13). 5335–5343. 19 indexed citations
10.
Sun, Ruoyu, et al.. (2021). Separation of Metabolites and Macromolecules for Short-TE 1H-MRSI Using Learned Component-Specific Representations. IEEE Transactions on Medical Imaging. 40(4). 1157–1167. 13 indexed citations
11.
Lam, Fan, Yudu Li, Rong Guo, Bryan Clifford, & Zhi‐Pei Liang. (2019). Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces. Magnetic Resonance in Medicine. 83(2). 377–390. 39 indexed citations
12.
Lam, Fan & Zhi‐Pei Liang. (2014). A subspace approach to high‐resolution spectroscopic imaging. Magnetic Resonance in Medicine. 71(4). 1349–1357. 104 indexed citations
13.
Wu, Zhenghua, Fan Lam, Chao Ma, & Zhi‐Pei Liang. (2014). Improved image reconstruction for subspace-based spectroscopic imaging using non-quadratic regularization. PubMed. 2014. 2432–2435. 3 indexed citations
14.
Holtrop, Joseph L., Fan Lam, Justin P. Haldar, et al.. (2013). More IMPATIENT: A gridding-accelerated Toeplitz-based strategy for non-Cartesian high-resolution 3D MRI on GPUs. Journal of Parallel and Distributed Computing. 73(5). 686–697. 29 indexed citations
15.
Lam, Fan, S. Derin Babacan, Justin P. Haldar, et al.. (2013). Denoising diffusion‐weighted magnitude MR images using rank and edge constraints. Magnetic Resonance in Medicine. 71(3). 1272–1284. 60 indexed citations
16.
Du, Huiqian & Fan Lam. (2012). Compressed sensing MR image reconstruction using a motion-compensated reference. Magnetic Resonance Imaging. 30(7). 954–963. 23 indexed citations
17.
Babacan, S. Derin, Fan Lam, Xi Peng, N. Minh, & Zhi‐Pei Liang. (2012). Interventional MRI with sparse sampling using union-of-subspaces. Europe PMC (PubMed Central). 314–317. 4 indexed citations
18.
Zhang, Haosen, Yongfei Wu, Lesley M. Foley, et al.. (2009). Real-time cardiac MRI using prior spatial-spectral information. PubMed. 2009. 4383–4386. 23 indexed citations
19.
Chang, Chunqi, Sze Fong Yau, P.C.K. Kwok, Fan Lam, & F.H.Y. Chan. (2002). Sequential approach to blind source separation using second order statistics. The HKU Scholars Hub (University of Hong Kong). 3. 1608–1612. 10 indexed citations
20.
Chan, F.H.Y., et al.. (1995). Thyroid Diagnosis by Thermogram Sequence Analysis. Bio-Medical Materials and Engineering. 5(3). 169–183. 4 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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