Terrence D. Ruddy
About
Terrence D. Ruddy is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Biomedical Engineering.
According to data from OpenAlex, Terrence D. Ruddy has authored 242 papers receiving a total of 7.2k indexed citations (citations by other indexed papers that have themselves been cited), including 189 papers in Radiology, Nuclear Medicine and Imaging, 93 papers in Cardiology and Cardiovascular Medicine and 59 papers in Biomedical Engineering. Recurrent topics in Terrence D. Ruddy's work include Cardiac Imaging and Diagnostics (175 papers), Medical Imaging Techniques and Applications (73 papers) and Advanced MRI Techniques and Applications (70 papers). Terrence D. Ruddy is often cited by papers focused on Cardiac Imaging and Diagnostics (175 papers), Medical Imaging Techniques and Applications (73 papers) and Advanced MRI Techniques and Applications (70 papers). Terrence D. Ruddy collaborates with scholars based in Canada, United States and Switzerland. Terrence D. Ruddy's co-authors include Rob Beanlands, Robert A. deKemp, Benjamin J.W. Chow, Kathryn Williams, R. Glenn Wells, Ann Guo, William G. Williams, Mark Henderson, Harry Rakowski and E.Douglas Wigle and has published in prestigious journals such as New England Journal of Medicine, Circulation and Nature Communications.
In The Last Decade
Terrence D. Ruddy
232 papers receiving 7.0k citations
Hit Papers
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late) cites · hero ref
| Name | h | Career | Trend | Papers | Cites | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Terrence D. Ruddy Canada | 41 | 4.7k | 3.3k | 1.4k | 1.4k | 588 | 242 | 7.2k | ||
| W. Gregory Hundley United States | 45 | 2.8k 0.6× | 5.0k 1.5× | 910 0.6× | 523 0.4× | 775 1.3× | 144 | 6.8k | ||
| Steven B. Feinstein United States | 44 | 2.2k 0.5× | 3.0k 0.9× | 1.5k 1.0× | 1.7k 1.2× | 2.0k 3.5× | 128 | 6.8k | ||
| Warren R. Janowitz United States | 15 | 4.8k 1.0× | 3.4k 1.0× | 2.0k 1.4× | 1.5k 1.1× | 1.3k 2.1× | 41 | 7.5k | ||
| Rosa Sicari Italy | 46 | 4.0k 0.9× | 7.0k 2.1× | 2.6k 1.8× | 670 0.5× | 1.7k 2.9× | 187 | 9.6k | ||
| Kenichi Tsujita Japan | 38 | 1.7k 0.4× | 2.9k 0.9× | 1.9k 1.3× | 405 0.3× | 836 1.4× | 435 | 5.8k | ||
| Linda K. Shaw United States | 46 | 2.5k 0.5× | 6.5k 2.0× | 3.1k 2.2× | 654 0.5× | 882 1.5× | 178 | 9.0k | ||
| Roxy Senior United Kingdom | 51 | 4.4k 0.9× | 6.1k 1.9× | 1.7k 1.2× | 1.6k 1.1× | 787 1.3× | 310 | 8.7k | ||
| Satoshi Yasuda Japan | 48 | 1.9k 0.4× | 5.8k 1.8× | 2.8k 2.0× | 651 0.5× | 1.6k 2.8× | 493 | 9.0k | ||
| Jon Hainer United States | 37 | 3.5k 0.8× | 3.2k 1.0× | 1.6k 1.1× | 605 0.4× | 881 1.5× | 101 | 5.8k | ||
| N R Zusmer United States | 5 | 3.7k 0.8× | 2.9k 0.9× | 1.6k 1.1× | 1.1k 0.8× | 1.1k 1.8× | 9 | 6.1k |
Countries citing papers authored by Terrence D. Ruddy
Since
Specialization
Citations
This map shows the geographic impact of Terrence D. Ruddy'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 Terrence D. Ruddy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Terrence D. Ruddy more than expected).
Fields of papers citing papers by Terrence D. Ruddy
Since
Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences
This network shows the impact of papers produced by Terrence D. Ruddy. 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 Terrence D. Ruddy. The network helps show where Terrence D. Ruddy may publish in the future.
Co-authorship network of co-authors of Terrence D. Ruddy
This figure shows the co-authorship network connecting the top 25 collaborators of Terrence D. Ruddy. A scholar is included among the top collaborators of Terrence D. Ruddy 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 Terrence D. Ruddy. Terrence D. Ruddy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
1.
Shanbhag, Aakash, Robert J.H. Miller, Mark A. Lemley, et al.. (2025). General Purpose Deep Learning Attenuation Correction Improves Diagnostic Accuracy of SPECT MPI. JACC. Cardiovascular imaging. 18(11). 1235–1246.
2.
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.
3.
Kadoya, Yoshito, Kevin E. Boczar, Benjamin J.W. Chow, et al.. (2025). Prognostic Utility of Quantitative Perfusion PET in Patients With Prior CABG: Incremental Value of Myocardial Flow Reserve and Coronary Vascular Resistance. Circulation Cardiovascular Imaging. 18(12). e018204–e018204.
4.
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.
5.
Kadoya, Yoshito, Gary R. Small, & Terrence D. Ruddy. (2024). WITHDRAWN: Image challenge: Noninvasive diagnosis of a massive cardiac lipoma with multimodality imaging. Journal of Nuclear Cardiology. 101798–101798.
6.
Kadoya, Yoshito, Aun‐Yeong Chong, Gary R. Small, et al.. (2024). Myocardial flow reserve recovery in patients with Takotsubo syndrome: Insights from positron emission tomography. Journal of Nuclear Cardiology. 37. 101869–101869.
7.
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.
8.
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.
Wells, R. Glenn, Frank M. Bengel, Luca Camoni, et al.. (2023). Multicenter Evaluation of the Feasibility of Clinical Implementation of SPECT Myocardial Blood Flow Measurement: Intersite Variability and Imaging Time. Circulation Cardiovascular Imaging. 16(10). e015009–e015009.
10.
Miller, Robert J.H., Tali Sharir, Andrew J. Einstein, et al.. (2022). Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry. Computers in Biology and Medicine. 145. 105449–105449.
11.
Tamarappoo, Balaji, Yuka Otaki, Tali Sharir, et al.. (2022). Differences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study. Circulation Cardiovascular Imaging. 15(6). e012741–e012741.
12.
Inácio, João R., Terrence D. Ruddy, Robert A. deKemp, et al.. (2022). Static CT myocardial perfusion imaging: image quality, artifacts including distribution and diagnostic performance compared to 82Rb PET. SHILAP Revista de lepidopterología. 6(1). 1–1.
13.
Hu, Lien-Hsin, Robert J.H. Miller, Tali Sharir, et al.. (2020). Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT. European Heart Journal - Cardiovascular Imaging. 22(6). 705–714.
14.
Schindler, Thomas H., Timothy M. Bateman, Daniel S. Berman, et al.. (2020). Appropriate Use Criteria for PET Myocardial Perfusion Imaging. Journal of Nuclear Medicine. 61(8). 1221–1265.
15.
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.
Haider, Bilal, Homer Yang, Alan Chaput, et al.. (2011). Prose 3: Early Ischemia Detection Using Digitized ST Values. 34.
17.
Ziadi, María Cecilia, Robert A. deKemp, Jennifer M. Renaud, et al.. (2009). Abstract 517: FDG PET Imaging Positively Impacts Management Direction and Predicts Outcomes in a Multicenter 'Real World' Setting. Circulation. 120.
18.
Chow, Benjamin J.W., Carole Dennie, Udo Hoffmann, et al.. (2007). Comparison of computed tomographic angiography versus rubidium-82 positron emission tomography for the detection of patients with anatomical coronary artery disease. Canadian Journal of Cardiology. 23(10). 801–807.
19.
Saab, George, David H. Birnie, Benjamin J.W. Chow, et al.. (2006). Is septal glucose metabolism altered in patients with left bundle branch block and ischemic cardiomyopathy?. PubMed. 47(11). 1763–8.
20.
Lindsay, Patrice, G G Barber, C W Cole, et al.. (1996). Perioperative ischaemia in aortic surgery: combined epidural/ general anaesthesia and epidural analgesia vs general anaesthesia andiv analgesia. Canadian Journal of Anesthesia/Journal canadien d anesthésie. 43(8). 769–777.
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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