Julian Gunn

6.4k total citations
148 papers, 4.4k citations indexed

About

Julian Gunn is a scholar working on Surgery, Cardiology and Cardiovascular Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Julian Gunn has authored 148 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 93 papers in Surgery, 74 papers in Cardiology and Cardiovascular Medicine and 57 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Julian Gunn's work include Coronary Interventions and Diagnostics (87 papers), Cardiac Imaging and Diagnostics (55 papers) and Acute Myocardial Infarction Research (35 papers). Julian Gunn is often cited by papers focused on Coronary Interventions and Diagnostics (87 papers), Cardiac Imaging and Diagnostics (55 papers) and Acute Myocardial Infarction Research (35 papers). Julian Gunn collaborates with scholars based in United Kingdom, Netherlands and United States. Julian Gunn's co-authors include Patricia V. Lawford, Javaid Iqbal, D. Rodney Hose, Paul Morris, David C. Crossman, Allison Morton, Patrick W. Serruys, Sheila Francis, Andrew Narracott and D.C. Cumberland and has published in prestigious journals such as The Lancet, Circulation and Journal of the American College of Cardiology.

In The Last Decade

Julian Gunn

141 papers receiving 4.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Julian Gunn United Kingdom 38 2.3k 1.9k 1.0k 804 665 148 4.4k
Juan F. Granada United States 39 3.2k 1.4× 2.9k 1.5× 1.1k 1.1× 1.6k 1.9× 359 0.5× 206 5.7k
Andrea Mariani United States 54 1.9k 0.8× 798 0.4× 330 0.3× 888 1.1× 924 1.4× 306 9.7k
Victor A. Ferrari United States 46 1.3k 0.6× 3.7k 2.0× 2.3k 2.3× 1.7k 2.1× 1.4k 2.1× 182 8.2k
Hyun‐Jae Kang South Korea 44 4.2k 1.8× 3.0k 1.6× 1.5k 1.5× 922 1.1× 2.9k 4.3× 316 9.1k
Shinichi Takamoto Japan 38 2.4k 1.0× 2.2k 1.1× 353 0.3× 2.2k 2.8× 382 0.6× 339 5.4k
Jos G. Maessen Netherlands 48 3.2k 1.4× 5.4k 2.8× 630 0.6× 1.1k 1.4× 1.1k 1.6× 459 9.8k
Jai Raman United States 36 2.1k 0.9× 2.1k 1.1× 265 0.3× 756 0.9× 343 0.5× 191 4.4k
Ariel Roguin Israel 41 2.0k 0.9× 4.3k 2.3× 2.4k 2.4× 1.0k 1.2× 610 0.9× 267 7.5k
Francisco Fernández‐Avilés Spain 49 4.1k 1.7× 7.1k 3.7× 2.2k 2.1× 835 1.0× 1.3k 2.0× 447 10.8k
Raffi Bekeredjian Germany 45 1.6k 0.7× 3.8k 2.0× 993 1.0× 705 0.9× 1.2k 1.8× 242 6.6k

Countries citing papers authored by Julian Gunn

Since Specialization
Citations

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

Fields of papers citing papers by Julian Gunn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Julian Gunn

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

All Works

20 of 20 papers shown
1.
Taylor, Daniel, Thiruni Adikari, Tom Newman, et al.. (2025). Invasive validation of novel 1D models for computation of coronary fractional flow reserve. Cardiovascular Research. 121(14). 2233–2245.
2.
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Taylor, Daniel, Andrew Narracott, Tom Newman, et al.. (2025). Derivation and sensitivity analysis of a novel one-dimensional model of coronary blood flow accounting for vessel taper and boundary slip. American Journal of Physiology-Heart and Circulatory Physiology. 329(5). H1033–H1046. 1 indexed citations
4.
5.
Gosling, Rebecca, Gareth Williams, Samer Alabed, et al.. (2023). Quantifying Myocardial Blood Flow and Resistance Using 4D-Flow Cardiac Magnetic Resonance Imaging. Cardiology Research and Practice. 2023. 1–7. 1 indexed citations
6.
Newman, Tom, Dipankar Choudhury, Ian Halliday, et al.. (2023). Rapid virtual fractional flow reserve using 3D computational fluid dynamics. European Heart Journal - Digital Health. 4(4). 283–290. 3 indexed citations
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Gosling, Rebecca, Tom Newman, D. Rodney Hose, et al.. (2022). The Complementary Value of Absolute Coronary Flow in the Assessment of Patients with Ischaemic Heart Disease. Nature Cardiovascular Research. 1(7). 611–616. 6 indexed citations
9.
Hsiao, Sarah, Alberto Marzo, Andrew Narracott, et al.. (2022). Integrating particle tracking with computational fluid dynamics to assess haemodynamic perturbation by coronary artery stents. PLoS ONE. 17(7). e0271469–e0271469. 2 indexed citations
10.
Schenkel, Torsten, et al.. (2021). Three-dimensional single framework multicomponent lattice Boltzmann equation method for vesicle hydrodynamics. Physics of Fluids. 33(7). 5 indexed citations
11.
Gosling, Rebecca, Gareth Williams, David R. J. Hose, et al.. (2021). Operator-dependent variability of angiography-derived fractional flow reserve and the implications for treatment. European Heart Journal - Digital Health. 2(2). 263–270. 6 indexed citations
12.
Gosling, Rebecca, et al.. (2021). The new role of diagnostic angiography in coronary physiological assessment. Heart. 107(10). 783–789. 18 indexed citations
13.
Morris, Paul, Rebecca Gosling, Paul C. Evans, et al.. (2020). A novel method for measuring absolute coronary blood flow and microvascular resistance in patients with ischaemic heart disease. Cardiovascular Research. 117(6). 1567–1577. 35 indexed citations
14.
Morris, Paul, Nick Curzen, & Julian Gunn. (2020). Angiography‐Derived Fractional Flow Reserve: More or Less Physiology?. Journal of the American Heart Association. 9(6). e015586–e015586. 30 indexed citations
15.
Morris, Paul, et al.. (2018). The impact of Objective Mathematical Analysis during Fractional Flow Reserve measurement: results from the OMA-FFR study. EuroIntervention. 14(8). 935–941. 1 indexed citations
16.
Morris, Paul, Javaid Iqbal, Claudio Chiastra, et al.. (2018). Simultaneous kissing stents to treat unprotected left main stem coronary artery bifurcation disease; stent expansion, vessel injury, hemodynamics, tissue healing, restenosis, and repeat revascularization. Catheterization and Cardiovascular Interventions. 92(6). E381–E392. 26 indexed citations
17.
Iqbal, Javaid, Renaud Fay, David Adlam, et al.. (2014). Effect of Eplerenone in Percutaneous Coronary Intervention-Treated Post-Myocardial Infarction Patients with Left Ventricular Systolic Dysfunction: A Subanalysis of the EPHESUS Trial. European Journal of Heart Failure. 16(6). 685–691. 10 indexed citations
18.
19.
Rogers, Leoandra Onnie, Julian Gunn, Bibi Gerner, & Melissa Wake. (2007). Shoulder pain. -letter-. 36(11). 887. 1 indexed citations
20.
Narracott, Andrew, D. Rodney Hose, Patricia V. Lawford, & Julian Gunn. (2003). Measurement of the symmetry of in vitro stent expansion: a stereo-photogrammetric approach. Journal of Medical Engineering & Technology. 27(2). 59–70. 5 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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