Mark Lemley

1.2k total citations
34 papers, 714 citations indexed

About

Mark Lemley is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Mark Lemley has authored 34 papers receiving a total of 714 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Radiology, Nuclear Medicine and Imaging, 17 papers in Biomedical Engineering and 6 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Mark Lemley's work include Cardiac Imaging and Diagnostics (33 papers), Medical Imaging Techniques and Applications (19 papers) and Advanced X-ray and CT Imaging (17 papers). Mark Lemley is often cited by papers focused on Cardiac Imaging and Diagnostics (33 papers), Medical Imaging Techniques and Applications (19 papers) and Advanced X-ray and CT Imaging (17 papers). Mark Lemley collaborates with scholars based in United States, Canada and Poland. Mark Lemley's co-authors include Piotr J. Slomka, Daniel S. Berman, Mathews B. Fish, Guido Germano, Yuan Xu, Damini Dey, Yuka Otaki, Sean W. Hayes, James Gerlach and Heidi Gransar and has published in prestigious journals such as Nature Communications, Journal of the American College of Cardiology and Radiology.

In The Last Decade

Mark Lemley

31 papers receiving 709 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark Lemley United States	14	643	307	217	66	45	34	714
Gilberto J. Aquino United States	14	530 0.8×	341 1.1×	136 0.6×	68 1.0×	43 1.0×	35	619
Mathieu Rubeaux United States	13	392 0.6×	167 0.5×	110 0.5×	79 1.2×	17 0.4×	27	477
Majd Zreik Netherlands	5	351 0.5×	213 0.7×	116 0.5×	78 1.2×	27 0.6×	6	428
Jakob De Geer Sweden	10	494 0.8×	231 0.8×	186 0.9×	249 3.8×	34 0.8×	17	539
Maximilian J. Bauer United States	11	328 0.5×	176 0.6×	149 0.7×	151 2.3×	12 0.3×	16	393
Moritz C. Halfmann Germany	13	417 0.6×	347 1.1×	84 0.4×	38 0.6×	18 0.4×	49	525
Sanne G. M. van Velzen Netherlands	8	263 0.4×	148 0.5×	101 0.5×	52 0.8×	22 0.5×	14	308
Kranthi K. Kolli United States	13	235 0.4×	90 0.3×	225 1.0×	204 3.1×	20 0.4×	34	426
Josua A. Decker Germany	16	528 0.8×	476 1.6×	90 0.4×	93 1.4×	17 0.4×	57	634
Puneet Sharma United States	3	266 0.4×	103 0.3×	132 0.6×	143 2.2×	24 0.5×	5	296

Countries citing papers authored by Mark Lemley

Since Specialization

Citations

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

Fields of papers citing papers by Mark Lemley

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Lemley

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

López, Diana, Daniel Huck, Sanjay Divakaran, et al.. (2025). Utility of ¹⁸ F-Flurpiridaz PET Relative Flow Reserve in Differentiating Obstructive From Nonobstructive Coronary Artery Disease. Circulation Cardiovascular Imaging. 18(11). e018323–e018323.

Miller, Robert J.H., Paul Kavanagh, Aditya Killekar, et al.. (2025). Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure. Circulation Cardiovascular Imaging. 18(7). e018188–e018188. 2 indexed citations

Miller, Robert J.H., Aakash Shanbhag, Alan Rozanski, et al.. (2025). Integrating perfusion with AI–derived coronary calcium on CT attenuation scans to improve selection of low-risk studies for stress-only SPECT myocardial perfusion imaging. Journal of Nuclear Cardiology. 53. 102482–102482.

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.

Kuronuma, Keiichiro, Yuka Otaki, Serge D. Van Kriekinge, et al.. (2024). Automatic Motion Correction for Myocardial Blood Flow Estimation Improves Diagnostic Performance for Coronary Artery Disease in 18F-Flurpiridaz PET-MPI. Journal of Nuclear Cardiology. 38. 101970–101970. 1 indexed citations

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

Miller, Robert J.H., Aditya Killekar, Aakash Shanbhag, et al.. (2024). Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography. Nature Communications. 15(1). 2747–2747. 9 indexed citations

Kuronuma, Keiichiro, Robert J.H. Miller, Ananya Singh, et al.. (2024). Downward myocardial creep during stress PET imaging is inversely associated with mortality. European Journal of Nuclear Medicine and Molecular Imaging. 51(6). 1622–1631. 1 indexed citations

Killekar, Aditya, Robert J.H. Miller, Mark Lemley, et al.. (2024). AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening. Radiology. 312(3). e240541–e240541. 8 indexed citations

10.

Williams, Michelle C., Aakash Shanbhag, Jianhang Zhou, et al.. (2024). Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry. European Heart Journal - Cardiovascular Imaging. 25(7). 976–985. 9 indexed citations

11.

Miller, Robert J.H., Aakash Shanbhag, Aditya Killekar, et al.. (2024). AI-Defined Cardiac Anatomy Improves Risk Stratification of Hybrid Perfusion Imaging. JACC. Cardiovascular imaging. 17(7). 780–791. 13 indexed citations

12.

Fehér, Attila, Robert J.H. Miller, Aakash Shanbhag, et al.. (2024). Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging. Journal of Nuclear Medicine. 65(5). 768–774. 6 indexed citations

13.

Pieszko, Konrad, Aakash Shanbhag, Aditya Killekar, et al.. (2022). Deep Learning of Coronary Calcium Scores From PET/CT Attenuation Maps Accurately Predicts Adverse Cardiovascular Events. JACC. Cardiovascular imaging. 16(5). 675–687. 42 indexed citations

14.

Pieszko, Konrad, Aakash Shanbhag, Mark Lemley, et al.. (2022). Reproducibility of quantitative coronary calcium scoring from PET/CT attenuation maps: comparison to ECG-gated CT scans. European Journal of Nuclear Medicine and Molecular Imaging. 49(12). 4122–4132. 20 indexed citations

15.

Fehér, Attila, Konrad Pieszko, Robert J.H. Miller, et al.. (2022). Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging. Journal of Nuclear Cardiology. 30(2). 590–603. 18 indexed citations

16.

Pieszko, Konrad, Aakash Shanbhag, Aditya Killekar, et al.. (2022). Calcium scoring in low-dose ungated chest CT scans using convolutional long-short term memory networks. PubMed. 12032. 115–115. 7 indexed citations

17.

Shanbhag, Aakash, Robert J.H. Miller, Konrad Pieszko, et al.. (2022). Deep Learning–Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT. Journal of Nuclear Medicine. 64(3). 472–478. 30 indexed citations

18.

Betancur, Julián, Yuka Otaki, Manish Motwani, et al.. (2017). Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning. JACC. Cardiovascular imaging. 11(7). 1000–1009. 152 indexed citations

19.

Nakazato, Ryo, Piotr J. Slomka, Mathews B. Fish, et al.. (2014). Quantitative high-efficiency cadmium-zinc-telluride SPECT with dedicated parallel-hole collimation system in obese patients: Results of a multi-center study. Journal of Nuclear Cardiology. 22(2). 266–275. 31 indexed citations

20.

Xu, Yuan, Reza Arsanjani, Morgan A. Clond, et al.. (2012). Transient ischemic dilation for coronary artery disease in quantitative analysis of same-day sestamibi myocardial perfusion SPECT. Journal of Nuclear Cardiology. 19(3). 465–473. 30 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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