Benoît Presles

537 citations
30 papers · 332 · h-index 11

Impact in

Papers in

Benoît Presles

29 papers receiving 326 citations

Peers

Benoît Presles
Comparison fields: 5 of 72
  • Radiation 84
  • Radiology, Nuclear Medicine and Imaging 142
  • Computer Vision and Pattern Recognition 91
  • Pulmonary and Respiratory Medicine 73
  • Filtration and Separation 4
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Sijuan Huang China
Haibin Chen China
Jinghao Zhou United States
Gisèle Pereira United States
Thomy Mertzanidou United Kingdom
Junko Ota Japan
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Bartłomiej W. Papież United Kingdom
Weigang Hu China
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Citations per field
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Citations per year

Countries citing papers authored by Benoît Presles

Since Specialization
Citations

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

Fields of papers citing papers by Benoît Presles

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Benoît Presles, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Benoît Presles Line = papers co-authored together Benoît Presles links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201956
2 202328
3 201728
4 201027
5 201424
6 201621
7 201618
8 201518
9 201816
10 201914
11 202211
12 20149
13 20129
14 20217
15 20236
16 20116
17 20235
18 20235
19 20245
20 20104

About Benoît Presles

Benoît Presles is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Computer Vision and Pattern Recognition, Radiation and Biomedical Engineering, having authored 30 papers that have together received 332 indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (7 papers), Advanced Radiotherapy Techniques (7 papers), Medical Image Segmentation Techniques (6 papers), Aortic aneurysm repair treatments (6 papers), Radiomics and Machine Learning in Medical Imaging (6 papers), Aortic Disease and Treatment Approaches (6 papers), MRI in cancer diagnosis (4 papers) and Advanced X-ray and CT Imaging (3 papers). The work is most often cited by research in Radiation (84 citations), Radiology, Nuclear Medicine and Imaging (142 citations), Computer Vision and Pattern Recognition (91 citations), Pulmonary and Respiratory Medicine (73 citations) and Filtration and Separation (4 citations). Benoît Presles has collaborated with scholars based in France, United Kingdom and United States. Frequent co-authors include Johan Debayle, Marie‐Claude Biston, David Sarrut, Simon Rit, Alain Lalande, P. Pommier, Farhan Akram, Mohamed Abdel‐Nasser, Santiago Romaní and Jean‐Marc Vrigneaud. Their work appears in journals such as Physica Medica, Physics in Medicine and Biology, EJNMMI Research, EJNMMI Physics and Medical Physics.

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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