A. Largent

542 citations
13 papers · 378 · 1 hit paper · h-index 8

Impact in

Papers in

A. Largent

13 papers receiving 378 citations

A. Largent's Hit Papers

Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review 2021 · 136 citations
1360+1+3Years since publication4080120

Peers

A. Largent
Comparison fields: 5 of 39
  • Radiation 190
  • Radiology, Nuclear Medicine and Imaging 181
  • Health Informatics 8
  • Computer Vision and Pattern Recognition 51
  • Biophysics 10
Replace Thilo Sentker with:
Thilo Sentker Germany
A. Barateau France
Frank Zijlstra Netherlands
Xiao Liang United States
Donghwi Hwang South Korea
Alireza Kamali‐Asl Iran
Shaoyan Pan United States
Samuel Haaf United States
Yuya Onishi Japan
Filipa Guerreiro Netherlands
A. Largent relative to Thilo Sentker Germany Thilo Sentker's profile →
Citations per field
00.5×3.7×
Thilo Sentker · 1×
Citations per year

Countries citing papers authored by A. Largent

Since Specialization
Citations

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

Fields of papers citing papers by A. Largent

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside A. Largent, 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 A. Largent Line = papers co-authored together A. Largent links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1
Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review
Hit paper breakdown →
2021136
2 201967
3 202047
4 201843
5 202121
6 202019
7 201816
8 202213
9 20175
10 20174
11 20193
12 20182
13 20192

About A. Largent

A. Largent is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Pediatrics, Perinatology and Child Health and Pulmonary and Respiratory Medicine, having authored 13 papers that have together received 378 indexed citations. Recurring topics across this work include Advanced Radiotherapy Techniques (9 papers), Medical Imaging Techniques and Applications (6 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Advanced X-ray and CT Imaging (2 papers), Fetal and Pediatric Neurological Disorders (1 paper), MRI in cancer diagnosis (1 paper), Radiation Therapy and Dosimetry (1 paper) and Occupational and environmental lung diseases (1 paper). The work is most often cited by research in Radiation (190 citations), Radiology, Nuclear Medicine and Imaging (181 citations), Health Informatics (8 citations), Computer Vision and Pattern Recognition (51 citations) and Biophysics (10 citations). A. Largent has collaborated with scholars based in France, Australia and United States. Frequent co-authors include Oscar Acosta, R. de Crevoisier, Jean‐Claude Nunes, C. Lafond, A. Barateau, Hervé Saint‐Jalmes, J. Castelli, Peter B. Greer, Jason Dowling and Eugenia Mylona. Their work appears in journals such as Physica Medica, International Journal of Radiation Oncology*Biology*Physics, World Journal of Urology, Journal of Magnetic Resonance Imaging 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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