Ming-Kai Chen

2.4k total citations · 2 hit papers
51 papers, 1.4k citations indexed

About

Ming-Kai Chen is a scholar working on Radiology, Nuclear Medicine and Imaging, Physiology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Ming-Kai Chen has authored 51 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Radiology, Nuclear Medicine and Imaging, 16 papers in Physiology and 11 papers in Cellular and Molecular Neuroscience. Recurrent topics in Ming-Kai Chen's work include Medical Imaging Techniques and Applications (19 papers), Alzheimer's disease research and treatments (14 papers) and Radiomics and Machine Learning in Medical Imaging (12 papers). Ming-Kai Chen is often cited by papers focused on Medical Imaging Techniques and Applications (19 papers), Alzheimer's disease research and treatments (14 papers) and Radiomics and Machine Learning in Medical Imaging (12 papers). Ming-Kai Chen collaborates with scholars based in United States, China and Belgium. Ming-Kai Chen's co-authors include Richard E. Carson, Yiyun Huang, Nabeel Nabulsi, Sjoerd J. Finnema, Shu-fei Lin, David Matuskey, Mika Naganawa, Joël Mercier, Jonas Hannestad and Takuya Toyonaga and has published in prestigious journals such as PLoS ONE, NeuroImage and Biological Psychiatry.

In The Last Decade

Ming-Kai Chen

50 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Imaging synaptic density in the living human brain

2016 361 citations Sjoerd J. Finnema, Nabeel Nabulsi et al. profile →
Assessing Synaptic Density in Alzheimer Disease With Synaptic Vesicle Glycoprotein 2A Positron Emission Tomographic Imaging

2018 298 citations Ming-Kai Chen, Adam P. Mecca et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ming-Kai Chen United States	15	512	430	426	332	272	51	1.4k
Takuya Toyonaga United States	21	644 1.3×	544 1.3×	595 1.4×	385 1.2×	337 1.2×	95	1.8k
Yihui Guan China	27	327 0.6×	516 1.2×	589 1.4×	311 0.9×	280 1.0×	152	2.2k
Dai Fukumoto Japan	27	503 1.0×	275 0.6×	178 0.4×	170 0.5×	78 0.3×	45	1.5k
Geoffrey L. Curran United States	12	450 0.9×	340 0.8×	536 1.3×	148 0.4×	94 0.3×	12	1.3k
Yihuan Lu United States	15	265 0.5×	601 1.4×	249 0.6×	169 0.5×	172 0.6×	56	1.1k
Päivi Marjamäki Finland	22	353 0.7×	303 0.7×	270 0.6×	137 0.4×	102 0.4×	49	1.6k
Matthew J. Betts Germany	17	382 0.7×	408 0.9×	298 0.7×	476 1.4×	159 0.6×	37	1.4k
Maiko Ono Japan	22	437 0.9×	247 0.6×	931 2.2×	122 0.4×	236 0.9×	61	1.8k
Ian F. Harrison United Kingdom	20	781 1.5×	481 1.1×	286 0.7×	179 0.5×	98 0.4×	36	1.8k
Laure Saint‐Aubert France	20	188 0.4×	323 0.8×	895 2.1×	326 1.0×	569 2.1×	41	1.5k

Countries citing papers authored by Ming-Kai Chen

Since Specialization

Citations

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

Fields of papers citing papers by Ming-Kai Chen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming-Kai Chen. 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 Ming-Kai Chen. The network helps show where Ming-Kai Chen may publish in the future.

Co-authorship network of co-authors of Ming-Kai Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Ming-Kai Chen. A scholar is included among the top collaborators of Ming-Kai Chen 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 Ming-Kai Chen. Ming-Kai Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Chen, Ming-Kai, Huidong Xie, Wei Ji, et al.. (2025). Anatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging. Medical Image Analysis. 107(Pt B). 103831–103831.

Fang, Xiaotian T., Nakul Ravi Raval, Ryan S. O’Dell, et al.. (2024). Synaptic density patterns in early Alzheimer’s disease assessed by independent component analysis. Brain Communications. 6(2). fcae107–fcae107. 5 indexed citations

Tsai, Yu‐Jung, Jean‐Dominique Gallezot, Xueqi Guo, et al.. (2024). Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification. Medical Image Analysis. 95. 103180–103180. 5 indexed citations

O’Dell, Ryan S., Ming-Kai Chen, Pradeep Varma, et al.. (2023). Assessment of Gray Matter Microstructure and Synaptic Density in Alzheimer's Disease: A Multimodal Imaging Study With DTI and SV2A PET. American Journal of Geriatric Psychiatry. 32(1). 17–28. 12 indexed citations

Zakiniaeiz, Yasmin, Hong Gao, Soheila Najafzadeh, et al.. (2022). Systemic inflammation enhances stimulant-induced striatal dopamine elevation in tobacco smokers. Brain Behavior and Immunity. 106. 262–269. 3 indexed citations

Toyonaga, Takuya, Luyao Shi, David Ménard, et al.. (2022). Deep learning–based attenuation correction for whole-body PET — a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. European Journal of Nuclear Medicine and Molecular Imaging. 49(9). 3086–3097. 12 indexed citations

Carson, Richard E., Mika Naganawa, Takuya Toyonaga, et al.. (2022). Imaging of Synaptic Density in Neurodegenerative Disorders. Journal of Nuclear Medicine. 63(Supplement 1). 60S–67S. 52 indexed citations

Malinis, Maricar, et al.. (2022). Real-world assessment of the clinical utility of whole body 18F-FDG PET/CT in the diagnosis of infection. PLoS ONE. 17(11). e0277403–e0277403. 3 indexed citations

Naganawa, Mika, Jean‐Dominique Gallezot, Sjoerd J. Finnema, et al.. (2022). Drug characteristics derived from kinetic modeling: combined 11C-UCB-J human PET imaging with levetiracetam and brivaracetam occupancy of SV2A. EJNMMI Research. 12(1). 71–71. 8 indexed citations

10.

Zakiniaeiz, Yasmin, Hong Gao, Soheila Najafzadeh, et al.. (2022). Nicotine Patch Alters Patterns of Cigarette Smoking-Induced Dopamine Release: Patterns Relate to Biomarkers Associated With Treatment Response. Nicotine & Tobacco Research. 24(10). 1597–1606. 5 indexed citations

11.

O’Dell, Ryan S., Adam P. Mecca, Ming-Kai Chen, et al.. (2021). Association of Aβ deposition and regional synaptic density in early Alzheimer’s disease: a PET imaging study with [11C]UCB-J. Alzheimer s Research & Therapy. 13(1). 11–11. 72 indexed citations

12.

Lu, Yihuan, Takuya Toyonaga, Mika Naganawa, et al.. (2021). Partial volume correction analysis for 11C-UCB-J PET studies of Alzheimer's disease. NeuroImage. 238. 118248–118248. 24 indexed citations

13.

Cheng, David, Monica Ghita, David Ménard, & Ming-Kai Chen. (2021). Determining the Minimal Required Ultra-Low-Dose CT Dose Level for Reliable Attenuation Correction of¹⁸F-FDG PET/CT: A Phantom Study. Journal of Nuclear Medicine Technology. 50(2). 126–131. 2 indexed citations

14.

Liu, Hui, MingDe Lin, David Ménard, et al.. (2021). PET Image Denoising Using a Deep-Learning Method for Extremely Obese Patients. IEEE Transactions on Radiation and Plasma Medical Sciences. 6(7). 766–770. 10 indexed citations

15.

Mecca, Adam P., Julia W. McDonald, Hannah R. Michalak, et al.. (2020). PET imaging of mGluR5 in Alzheimer’s disease. Alzheimer s Research & Therapy. 12(1). 15–15. 40 indexed citations

16.

Naganawa, Mika, Jean‐Dominique Gallezot, Vijay Shah, et al.. (2020). Assessment of population-based input functions for Patlak imaging of whole body dynamic 18F-FDG PET. EJNMMI Physics. 7(1). 67–67. 50 indexed citations

17.

Sanchez‐Rangel, Elizabeth, Jean‐Dominique Gallezot, Catherine W. Yeckel, et al.. (2019). Norepinephrine transporter availability in brown fat is reduced in obesity: a human PET study with [11C] MRB. International Journal of Obesity. 44(4). 964–967. 19 indexed citations

18.

Wu, Jing, Yihuan Lu, Mika Naganawa, et al.. (2018). Improved discrimination between benign and malignant LDCT screening-detected lung nodules with dynamic over static ¹⁸F-FDG PET as a function of injected dose. Physics in Medicine and Biology. 63(17). 175015–175015. 21 indexed citations

19.

Park, Eunkyung, Jean‐Dominique Gallezot, Shuang Liu, et al.. (2015). 11C-PBR28 imaging in multiple sclerosis patients and healthy controls: test-retest reproducibility and focal visualization of active white matter areas. European Journal of Nuclear Medicine and Molecular Imaging. 42(7). 1081–1092. 73 indexed citations

20.

Chen, Ming-Kai & David Cheng. (2014). What is the role of dosimetry in patients with advanced thyroid cancer?. Current Opinion in Oncology. 27(1). 33–37. 6 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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