Charline Lasnon

1.6k total citations
42 papers, 973 citations indexed

About

Charline Lasnon is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Charline Lasnon has authored 42 papers receiving a total of 973 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Pulmonary and Respiratory Medicine and 13 papers in Biomedical Engineering. Recurrent topics in Charline Lasnon's work include Medical Imaging Techniques and Applications (33 papers), Radiomics and Machine Learning in Medical Imaging (27 papers) and Advanced X-ray and CT Imaging (13 papers). Charline Lasnon is often cited by papers focused on Medical Imaging Techniques and Applications (33 papers), Radiomics and Machine Learning in Medical Imaging (27 papers) and Advanced X-ray and CT Imaging (13 papers). Charline Lasnon collaborates with scholars based in France, Luxembourg and Australia. Charline Lasnon's co-authors include Nicolas Aide, Bernhard Sattler, Patrick Veit‐Haibach, Teréz Séra, Ronald Boellaard, Cédric Desmonts, Elske Quak, Pascal Dô, Christophe Fruchart and Anne‐Claire Gac and has published in prestigious journals such as Scientific Reports, Radiology and Journal of Nuclear Medicine.

In The Last Decade

Charline Lasnon

40 papers receiving 966 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Charline Lasnon France 17 781 278 212 164 137 42 973
Milton J. Guiberteau United States 11 728 0.9× 266 1.0× 134 0.6× 114 0.7× 68 0.5× 34 949
Marc Lemort Belgium 17 555 0.7× 342 1.2× 41 0.2× 147 0.9× 88 0.6× 68 1.1k
Sandra Rosenbaum Germany 13 375 0.5× 287 1.0× 55 0.3× 202 1.2× 36 0.3× 18 793
L. Diggles United States 17 676 0.9× 235 0.8× 49 0.2× 188 1.1× 251 1.8× 30 902
Donna Taylor Australia 15 302 0.4× 279 1.0× 76 0.4× 160 1.0× 239 1.7× 75 797
H. McCallum United Kingdom 15 576 0.7× 179 0.6× 262 1.2× 97 0.6× 37 0.3× 45 947
David S. Yoo United States 17 373 0.5× 423 1.5× 58 0.3× 163 1.0× 49 0.4× 39 841
V. Bishay United States 11 193 0.2× 276 1.0× 36 0.2× 92 0.6× 65 0.5× 44 748
Ken Herrmann Germany 10 287 0.4× 211 0.8× 57 0.3× 144 0.9× 14 0.1× 61 562
Stéphanie Lamart France 11 316 0.4× 251 0.9× 89 0.4× 136 0.8× 52 0.4× 29 651

Countries citing papers authored by Charline Lasnon

Since Specialization
Citations

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

Fields of papers citing papers by Charline Lasnon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Charline Lasnon

This figure shows the co-authorship network connecting the top 25 collaborators of Charline Lasnon. A scholar is included among the top collaborators of Charline Lasnon 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 Charline Lasnon. Charline Lasnon 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.
Quak, Elske, Idlir Licaj, Renaud Ciappuccini, et al.. (2023). Deep Learning Denoising Improves and Homogenizes Patient [18F]FDG PET Image Quality in Digital PET/CT. Diagnostics. 13(9). 1626–1626. 3 indexed citations
2.
Desmonts, Cédric, Charline Lasnon, Cyril Jaudet, & Nicolas Aide. (2023). PET imaging and quantification of small animals using a clinical SiPM-based camera. EJNMMI Physics. 10(1). 61–61. 2 indexed citations
3.
Quak, Elske, et al.. (2023). Artificial intelligence-based 68Ga-DOTATOC PET denoising for optimizing 68Ge/68Ga generator use throughout its lifetime. Frontiers in Medicine. 10. 1137514–1137514. 2 indexed citations
4.
Lasnon, Charline, Renaud Ciappuccini, Aurélien Corroyer‐Dulmont, et al.. (2022). Artificial intelligence-based PET denoising could allow a two-fold reduction in [18F]FDG PET acquisition time in digital PET/CT. European Journal of Nuclear Medicine and Molecular Imaging. 49(11). 3750–3760. 32 indexed citations
5.
Lasnon, Charline, et al.. (2021). Female Authors in Nuclear Medicine Journals: A Survey from 2014 to 2020. Journal of Nuclear Medicine. 63(7). 995–1000. 3 indexed citations
6.
Aide, Nicolas, Laurent Poulain, Nicolas Elie, et al.. (2021). A PSMA-targeted theranostic approach is unlikely to be efficient in serous ovarian cancers. EJNMMI Research. 11(1). 11–11. 10 indexed citations
7.
Lasnon, Charline, et al.. (2020). How fast can we scan patients with modern (digital) PET/CT systems?. European Journal of Radiology. 129. 109144–109144. 24 indexed citations
8.
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Lasnon, Charline, Idlir Licaj, Pascal Dô, et al.. (2018). Why harmonization is needed when using FDG PET/CT as a prognosticator: demonstration with EARL-compliant SUV as an independent prognostic factor in lung cancer. European Journal of Nuclear Medicine and Molecular Imaging. 46(2). 421–428. 28 indexed citations
11.
Lasnon, Charline, et al.. (2017). Impact of the EARL harmonization program on automatic delineation of metabolic active tumour volumes (MATVs). EJNMMI Research. 7(1). 30–30. 24 indexed citations
12.
Aide, Nicolas, et al.. (2017). Diagnostic and prognostic value of baseline FDG PET/CT skeletal textural features in diffuse large B cell lymphoma. European Journal of Nuclear Medicine and Molecular Imaging. 45(5). 699–711. 39 indexed citations
13.
Quak, Elske, Pierre‐Yves Le Roux, Charline Lasnon, et al.. (2016). Does PET SUV Harmonization Affect PERCIST Response Classification?. Journal of Nuclear Medicine. 57(11). 1699–1706. 28 indexed citations
14.
Lasnon, Charline, Cédric Desmonts, Pascal Dô, et al.. (2016). Generating harmonized SUV within the EANM EARL accreditation program: software approach versus EARL-compliant reconstruction. Annals of Nuclear Medicine. 31(2). 125–134. 34 indexed citations
15.
Lasnon, Charline, Mohamed Majdoub, Pascal Dô, et al.. (2016). 18F-FDG PET/CT heterogeneity quantification through textural features in the era of harmonisation programs: a focus on lung cancer. European Journal of Nuclear Medicine and Molecular Imaging. 43(13). 2324–2335. 45 indexed citations
16.
Lasnon, Charline, Audrey Emmanuelle Dugué, Mélanie Briand, et al.. (2014). NEMA NU 4-Optimized Reconstructions for Therapy Assessment in Cancer Research with the Inveon Small Animal PET/CT System. Molecular Imaging and Biology. 17(3). 403–412. 14 indexed citations
17.
Lasnon, Charline, Cédric Desmonts, Elske Quak, et al.. (2013). Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients. European Journal of Nuclear Medicine and Molecular Imaging. 40(7). 985–996. 88 indexed citations
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Lasnon, Charline, Rodney J. Hicks, Jean‐Mathieu Beauregard, et al.. (2012). Impact of Point Spread Function Reconstruction on Thoracic Lymph Node Staging With 18F-FDG PET/CT in Non–Small Cell Lung Cancer. Clinical Nuclear Medicine. 37(10). 971–976. 47 indexed citations
20.
Aide, Nicolas, Mélanie Briand, Pierre Bohn, et al.. (2010). αvβ3 imaging can accurately distinguish between mature teratoma and necrosis in 18F-FDG-negative residual masses after treatment of non-seminomatous testicular cancer: a preclinical study. European Journal of Nuclear Medicine and Molecular Imaging. 38(2). 323–333. 8 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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