Damini Dey

20.3k total citations · 2 hit papers
446 papers, 10.7k citations indexed

About

Damini Dey is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Surgery. According to data from OpenAlex, Damini Dey has authored 446 papers receiving a total of 10.7k indexed citations (citations by other indexed papers that have themselves been cited), including 342 papers in Radiology, Nuclear Medicine and Imaging, 226 papers in Cardiology and Cardiovascular Medicine and 146 papers in Surgery. Recurrent topics in Damini Dey's work include Cardiac Imaging and Diagnostics (318 papers), Cardiovascular Disease and Adiposity (144 papers) and Advanced X-ray and CT Imaging (132 papers). Damini Dey is often cited by papers focused on Cardiac Imaging and Diagnostics (318 papers), Cardiovascular Disease and Adiposity (144 papers) and Advanced X-ray and CT Imaging (132 papers). Damini Dey collaborates with scholars based in United States, United Kingdom and Canada. Damini Dey's co-authors include Piotr J. Slomka, Daniel S. Berman, Balaji Tamarappoo, Heidi Gransar, Stephan Achenbach, Ryo Nakazato, Guido Germano, Victor Cheng, Sebastien Cadet and Sean W. Hayes and has published in prestigious journals such as Circulation, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Damini Dey

417 papers receiving 10.5k 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.

Artificial Intelligence in Cardiovascular Imaging

2019 378 citations Damini Dey, Piotr J. Slomka et al. Journal of the American College of Cardiology profile →
Association of Lipoprotein(a) With Atherosclerotic Plaque Progression

2022 99 citations Evangelos Tzolos, Jacek Kwieciński et al. Journal of the American College of Cardiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Damini Dey United States	55	6.9k	5.5k	3.7k	2.7k	1.3k	446	10.7k
Piotr J. Slomka United States	63	10.7k 1.6×	5.6k 1.0×	3.2k 0.9×	4.2k 1.6×	1.4k 1.1×	520	14.5k
James H.F. Rudd United Kingdom	54	5.0k 0.7×	3.7k 0.7×	2.5k 0.7×	868 0.3×	4.6k 3.6×	174	11.0k
Bart Bijnens Spain	60	4.0k 0.6×	8.5k 1.6×	1.7k 0.5×	1.1k 0.4×	1.8k 1.4×	378	11.7k
Rozemarijn Vliegenthart Netherlands	50	5.9k 0.9×	2.0k 0.4×	1.3k 0.3×	2.2k 0.8×	4.5k 3.6×	316	9.8k
Samuel J. Asirvatham United States	54	1.5k 0.2×	11.6k 2.1×	1.9k 0.5×	729 0.3×	1.1k 0.9×	575	13.7k
Sven Plein United Kingdom	59	10.8k 1.6×	10.1k 1.9×	3.0k 0.8×	1.6k 0.6×	1.6k 1.3×	497	16.3k
Gianluca Pontone Italy	41	4.2k 0.6×	4.5k 0.8×	2.1k 0.6×	1.4k 0.5×	1.0k 0.8×	403	7.3k
Stefan K. Piechnik United Kingdom	56	6.6k 1.0×	6.5k 1.2×	1.3k 0.3×	551 0.2×	785 0.6×	217	12.4k
Pim A. de Jong Netherlands	58	5.3k 0.8×	1.0k 0.2×	1.8k 0.5×	2.5k 0.9×	6.5k 5.1×	397	12.8k
Peter M. A. van Ooijen Netherlands	41	3.5k 0.5×	681 0.1×	1.3k 0.4×	1.4k 0.5×	2.0k 1.6×	257	6.0k

Countries citing papers authored by Damini Dey

Since Specialization

Citations

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

Fields of papers citing papers by Damini Dey

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Damini Dey

This figure shows the co-authorship network connecting the top 25 collaborators of Damini Dey. A scholar is included among the top collaborators of Damini Dey 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 Damini Dey. Damini Dey 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

Tomasino, Guadalupe Flores, Caroline Park, Kajetan Grodecki, et al.. (2025). Coronary plaque characteristics quantified by artificial intelligence-enabled plaque analysis: Insights from a multi-ethnic asymptomatic US population. American Journal of Preventive Cardiology. 21. 100929–100929. 1 indexed citations

Kinoshita, Daisuke, Keishi Suzuki, Daichi Fujimoto, et al.. (2025). High-risk plaque features and perivascular inflammation. Journal of cardiovascular computed tomography. 19(3). 299–305. 1 indexed citations

Filtz, Annalisa, Eduardo Gil, Leslee J. Shaw, et al.. (2025). Epicardial adipose tissue, cardiac damage, and mortality in patients undergoing TAVR for aortic stenosis. The International Journal of Cardiovascular Imaging. 41(2). 279–290. 1 indexed citations

Filtz, Annalisa, Kajetan Grodecki, Matthew J. Miller, et al.. (2025). Novel CT-derived markers for enhanced long-term risk stratification in the planning of TAVR for aortic stenosis. Journal of cardiovascular computed tomography. 19(5). 502–511. 1 indexed citations

Miller, Robert J.H., Paul Kavanagh, Mark A. Lemley, et al.. (2025). Artificial Intelligence–Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging. Journal of Nuclear Medicine. 66(4). 648–653. 1 indexed citations

Miller, Robert J.H., Mark Lemley, Joanna X. Liang, et al.. (2025). Incremental diagnostic value of artificial intelligence-derived coronary artery calcium in 18F-flurpiridaz positron emission tomography myocardial perfusion imaging. Journal of Nuclear Cardiology. 54. 102532–102532.

Kagiyama, Nobuyuki, Márton Tokodi, Quincy A. Hathaway, et al.. (2025). PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation. JACC. Cardiovascular imaging. 19(2). 225–251. 1 indexed citations

Kuronuma, Keiichiro, Mark Hyun, Donghee Han, et al.. (2024). Head-to-head comparison of 18F-sodium fluoride coronary PET imaging between a silicon photomultiplier with digital photon counting and conventional scanners. Journal of Nuclear Cardiology. 42. 102045–102045. 1 indexed citations

Kolossváry, Márton, Andrew Lin, Jacek Kwieciński, et al.. (2024). Coronary Plaque Radiomic Phenotypes Predict Fatal or Nonfatal Myocardial Infarction. JACC. Cardiovascular imaging. 18(3). 308–319. 2 indexed citations

10.

Hong, Wei, Daniel S. Berman, Damini Dey, et al.. (2024). Coronary Artery Calcium Density and Risk of Cardiovascular Events. JACC. Cardiovascular imaging. 18(3). 294–304. 2 indexed citations

11.

Miller, Robert J.H., Aakash Shanbhag, Aditya Killekar, et al.. (2024). AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging. npj Digital Medicine. 7(1). 24–24. 15 indexed citations

12.

Kim, Jiwon, Jamie Layland, Kevin Cheng, et al.. (2023). Peri-Coronary Adipose Tissue Is a Predictor of Stent Failure in Patients Undergoing Percutaneous Coronary Intervention. Cardiovascular revascularization medicine. 53. 61–66. 1 indexed citations

13.

Kuronuma, Keiichiro, Robert J.H. Miller, Serge D. Van Kriekinge, et al.. (2023). Incremental prognostic value of stress phase entropy over standard PET myocardial perfusion imaging variables. European Journal of Nuclear Medicine and Molecular Imaging. 50(12). 3619–3629. 1 indexed citations

14.

Liu, Ting, Andrew G. Howarth, Yinyin Chen, et al.. (2022). Intramyocardial Hemorrhage and the “Wave Front” of Reperfusion Injury Compromising Myocardial Salvage. Journal of the American College of Cardiology. 79(1). 35–48. 49 indexed citations

15.

Kwan, Alan C., Heidi Gransar, Evangelos Tzolos, et al.. (2021). The accuracy of coronary CT angiography in patients with coronary calcium score above 1000 Agatston Units: Comparison with quantitative coronary angiography. Journal of cardiovascular computed tomography. 15(5). 412–418. 21 indexed citations

16.

Tzolos, Evangelos, Donghee Han, John D. Friedman, et al.. (2021). Detection of small coronary calcifications in patients with Agatston coronary artery calcium score of zero. Journal of cardiovascular computed tomography. 16(2). 150–154. 5 indexed citations

17.

Nørgaard, Bjarne Linde, Damini Dey, Jørgen Gram, et al.. (2020). Coronary flow impairment in asymptomatic patients with early stage type-2 diabetes: Detection by FFR_CT. Diabetes and Vascular Disease Research. 17(5). 3154019798–3154019798. 6 indexed citations

18.

Han, Donghee, Alan Rozanski, Heidi Gransar, et al.. (2019). Myocardial Ischemic Burden and Differences in Prognosis Among Patients With and Without Diabetes: Results From the Multicenter International REFINE SPECT Registry. Diabetes Care. 43(2). 453–459. 16 indexed citations

19.

Matsumoto, Hidenari, Satoshi Watanabe, Eisho Kyo, et al.. (2019). Improved Evaluation of Lipid-Rich Plaque at Coronary CT Angiography: Head-to-Head Comparison with Intravascular US. Radiology Cardiothoracic Imaging. 1(5). e190069–e190069. 10 indexed citations

20.

Dey, Damini, Annika Schuhbaeck, James K. Min, Daniel S. Berman, & Stephan Achenbach. (2013). Non-invasive measurement of coronary plaque from coronary CT angiography and its clinical implications. Expert Review of Cardiovascular Therapy. 11(8). 1067–1077. 11 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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