Aditya Killekar

1.1k total citations
18 papers, 189 citations indexed

About

Aditya Killekar is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Aditya Killekar has authored 18 papers receiving a total of 189 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 10 papers in Biomedical Engineering and 3 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Aditya Killekar's work include Cardiac Imaging and Diagnostics (11 papers), Advanced X-ray and CT Imaging (10 papers) and Medical Imaging Techniques and Applications (5 papers). Aditya Killekar is often cited by papers focused on Cardiac Imaging and Diagnostics (11 papers), Advanced X-ray and CT Imaging (10 papers) and Medical Imaging Techniques and Applications (5 papers). Aditya Killekar collaborates with scholars based in United States, Canada and Poland. Aditya Killekar's co-authors include Piotr J. Slomka, Damini Dey, Robert J.H. Miller, Daniel S. Berman, Joanna X. Liang, Aakash Shanbhag, Mark Lemley, Paul Kavanagh, Konrad Pieszko and Serge D. Van Kriekinge and has published in prestigious journals such as Nature Communications, Journal of the American College of Cardiology and Radiology.

In The Last Decade

Aditya Killekar

17 papers receiving 187 citations

Peers

Aditya Killekar

RNMI

CCM

PRM

Aakash Shanbhag United States

Iulia A. Popescu United Kingdom

Ananya Singh United States

Gabriel Maliakal United States

Nishith Khandwala United States

Ivar Mjåland Salte Norway

Ivan A. Blokhin Russia

E V Garcia United States

Georgios Rampidis Greece

Danielle M. Dargis United States

Aakash Shanbhag United States

Aditya Killekar

210 ×1.3

RNMI

111 ×1.4

84 ×1.3

CCM

14 ×0.7

8 ×0.6

PRM

Citations per year, relative to Aditya Killekar Aditya Killekar (= 1×) peers Aakash Shanbhag

Countries citing papers authored by Aditya Killekar

Since Specialization

Citations

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

Fields of papers citing papers by Aditya Killekar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aditya Killekar

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

All Works

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18 of 18 papers shown

Killekar, Aditya, et al.. (2025). Dynamic frame-by-frame motion correction for 18F-flurpiridaz PET-MPI using convolution neural network. European Journal of Nuclear Medicine and Molecular Imaging. 53(4). 2708–2720.

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

Killekar, Aditya, Robert J.H. Miller, Damini Dey, et al.. (2025). Priming with specific context improves large language model performance on nuclear cardiology board preparation test. Journal of Nuclear Cardiology. 52. 102269–102269. 1 indexed citations

Zhou, Jianhang, Aakash Shanbhag, Donghee Han, et al.. (2025). Automated proximal coronary artery calcium identification using artificial intelligence: advancing cardiovascular risk assessment. European Heart Journal - Cardiovascular Imaging. 26(3). 471–480. 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

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

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

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

10.

Shanbhag, Aakash, Konrad Pieszko, Robert J.H. Miller, et al.. (2023). Comparative analysis between convolutional long short-term memory networks and vision transformers for coronary calcium scoring in non-contrast CT. 12–12. 4 indexed citations

11.

Killekar, Aditya, Jacek Kwieciński, Mariusz Kruk, et al.. (2023). Pseudo-contrast cardiac CT angiography derived from non-contrast CT using conditional generative adversarial networks. 136–136. 1 indexed citations

12.

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

13.

Singh, Ananya, Jacek Kwieciński, Robert J.H. Miller, et al.. (2022). Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images. Circulation Cardiovascular Imaging. 15(9). e014526–e014526. 25 indexed citations

14.

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

15.

Singh, Ananya, Konrad Pieszko, Aakash Shanbhag, et al.. (2022). IMPROVED MORTALITY RISK ASSESSMENT FROM MYOCARDIAL PET FLOW, PERFUSION AND CALCIUM SCORES USING ARTIFICIAL INTELLIGENCE. Journal of the American College of Cardiology. 79(9). 1182–1182. 1 indexed citations

16.

Miller, Robert J.H., Konrad Pieszko, Aakash Shanbhag, et al.. (2022). Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events. Journal of Nuclear Medicine. 64(4). 652–658. 31 indexed citations

17.

Singh, Ananya, Jacek Kwieciński, Sebastien Cadet, et al.. (2022). Automated nonlinear registration of coronary PET to CT angiography using pseudo-CT generated from PET with generative adversarial networks. Journal of Nuclear Cardiology. 30(2). 604–615. 18 indexed citations

18.

Simon, Judit, Kajetan Grodecki, Aditya Killekar, et al.. (2022). Radiomorphological signs and clinical severity of SARS-CoV-2 lineage B.1.1.7. BJR|Open. 4(1). 20220016–20220016. 2 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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