C. Lafond
- Radiation top 0.5%
- Advanced Radiotherapy Techniques 53
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- Medical Imaging Techniques and Applications 25
- Radiation Dose and Imaging 14
- Radiomics and Machine Learning in Medical Imaging 12
- Otorhinolaryngology top 5%
- Head and Neck Cancer Studies 9
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- Prostate Cancer Diagnosis and Treatment 14
- Obstetrics and Gynecology top 10%
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- Advanced X-ray and CT Imaging 10
- Medical Imaging and Analysis 7
C. Lafond
69 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Radiation 724
- Radiology, Nuclear Medicine and Imaging 703
- Otorhinolaryngology 95
- Pulmonary and Respiratory Medicine 434
- Obstetrics and Gynecology 80
Countries citing papers authored by C. Lafond
This map shows the geographic impact of C. Lafond'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 C. Lafond with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites C. Lafond more than expected).
Fields of papers citing papers by C. Lafond
This network shows the impact of papers produced by C. Lafond. 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 C. Lafond. The network helps show where C. Lafond may publish in the future.
Co-authorship network
The 25 scholars most cited alongside C. Lafond, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 0 | |
| 2 | 2023 | 2 | |
| 3 | 2023 | 8 | |
| 4 | 2023 | 9 | |
| 5 | 2022 | 6 | |
| 6 | 2021 | 19 | |
| 7 | Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature reviewbreakdown → | 2021 | 125 |
| 8 | 2020 | 7 | |
| 9 | 2018 | 34 | |
| 10 | 2018 | 2 | |
| 11 | 2018 | 6 | |
| 12 | 2016 | 15 | |
| 13 | 2016 | 43 | |
| 14 | 2016 | 23 | |
| 15 | 2015 | 8 | |
| 16 | 2015 | 7 | |
| 17 | 2013 | 64 | |
| 18 | 2009 | 11 | |
| 19 | 2005 | 13 | |
| 20 | 2001 | 8 |
About C. Lafond
C. Lafond is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging, Otorhinolaryngology, Computational Mathematics and Pulmonary and Respiratory Medicine, having authored 75 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Radiotherapy Techniques (53 papers), Medical Imaging Techniques and Applications (25 papers), Radiation Dose and Imaging (14 papers), Prostate Cancer Diagnosis and Treatment (14 papers), Radiomics and Machine Learning in Medical Imaging (12 papers), Advanced X-ray and CT Imaging (10 papers), Head and Neck Cancer Studies (9 papers) and Medical Imaging and Analysis (7 papers). The work is most often cited by research in Radiation (724 citations), Radiology, Nuclear Medicine and Imaging (703 citations), Otorhinolaryngology (95 citations), Pulmonary and Respiratory Medicine (434 citations) and Obstetrics and Gynecology (80 citations). C. Lafond has collaborated with scholars based in France, Australia and Colombia. Frequent co-authors include R. de Crevoisier, Oscar Acosta, Antoine Simon, J. Castelli, Pascal Haigron, A. Barateau, Jean‐Claude Nunes, B. Rigaud, A. Largent and R. de Crevoisier. Their work appears in journals such as Physica Medica, International Journal of Radiation Oncology*Biology*Physics, Radiotherapy and Oncology, Medical Physics and Physics and Imaging in Radiation Oncology.
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.