Philippe Ciuciu

4.0k total citations
120 papers, 2.3k citations indexed

About

Philippe Ciuciu is a scholar working on Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience and Computational Mechanics. According to data from OpenAlex, Philippe Ciuciu has authored 120 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Radiology, Nuclear Medicine and Imaging, 56 papers in Cognitive Neuroscience and 33 papers in Computational Mechanics. Recurrent topics in Philippe Ciuciu's work include Advanced MRI Techniques and Applications (61 papers), Functional Brain Connectivity Studies (47 papers) and Sparse and Compressive Sensing Techniques (32 papers). Philippe Ciuciu is often cited by papers focused on Advanced MRI Techniques and Applications (61 papers), Functional Brain Connectivity Studies (47 papers) and Sparse and Compressive Sensing Techniques (32 papers). Philippe Ciuciu collaborates with scholars based in France, United States and United Kingdom. Philippe Ciuciu's co-authors include Jean‐Baptiste Poline, Jérôme Idier, Thomas Vincent, Bertrand Thirion, Patrice Abry, Stanislas Dehaene, Guillaume Marrelec, Habib Benali, Denis Le Bihan and Lotfi Chaâri and has published in prestigious journals such as Journal of Neuroscience, PLoS ONE and NeuroImage.

In The Last Decade

Philippe Ciuciu

115 papers receiving 2.2k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Philippe Ciuciu 1.4k 967 312 230 183 120 2.3k
Nelson J. Trujillo‐Barreto 3.1k 2.3× 525 0.5× 106 0.3× 99 0.4× 267 1.5× 70 3.7k
Arvid Lundervold 890 0.7× 1.2k 1.2× 265 0.8× 77 0.3× 179 1.0× 116 3.4k
Jukka Sarvas 2.2k 1.7× 733 0.8× 157 0.5× 62 0.3× 113 0.6× 56 3.9k
Eduardo Martínez‐Montes 1.2k 0.9× 448 0.5× 57 0.2× 77 0.3× 64 0.3× 49 1.6k
Martin Luessi 1.1k 0.8× 148 0.2× 83 0.3× 90 0.4× 55 0.3× 18 1.5k
Yi‐Ou Li 1.2k 0.9× 585 0.6× 72 0.2× 21 0.1× 125 0.7× 22 1.9k
Oluwasanmi Koyejo 1.6k 1.2× 638 0.7× 40 0.1× 41 0.2× 636 3.5× 81 2.8k
R. Biscay 836 0.6× 125 0.1× 79 0.3× 50 0.2× 138 0.8× 60 1.4k
Katarzyna J. Blinowska 3.5k 2.6× 221 0.2× 102 0.3× 33 0.1× 384 2.1× 119 4.5k
Jorge Riera 1.4k 1.0× 369 0.4× 30 0.1× 76 0.3× 73 0.4× 77 1.9k

Countries citing papers authored by Philippe Ciuciu

Since Specialization
Citations

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

Fields of papers citing papers by Philippe Ciuciu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philippe Ciuciu

This figure shows the co-authorship network connecting the top 25 collaborators of Philippe Ciuciu. A scholar is included among the top collaborators of Philippe Ciuciu 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 Philippe Ciuciu. Philippe Ciuciu 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.
Pan, Chunhong, et al.. (2025). MRI-NUFFT: Doing non-Cartesian MRI has never been easier. The Journal of Open Source Software. 10(108). 7743–7743.
2.
3.
Wang, Sheng H., Gabriele Arnulfo, Lino Nobili, et al.. (2024). Machine Learning Models Trained in a Low-Dimensional Latent Space for Epileptogenic Zone (EZ) Localization. SPIRE - Sciences Po Institutional REpository. 1586–1590. 1 indexed citations
4.
Nadar, Mariappan S., et al.. (2023). Deep learning‐assisted model‐based off‐resonance correction for non‐Cartesian SWI. Magnetic Resonance in Medicine. 90(4). 1431–1445. 1 indexed citations
5.
Massire, Aurélien, et al.. (2023). Improving spreading projection algorithm for rapid k‐space sampling trajectories through minimized off‐resonance effects and gridding of low frequencies. Magnetic Resonance in Medicine. 90(3). 1069–1085. 2 indexed citations
6.
Boulant, Nicolas, et al.. (2023). Impact of B0$$ {\mathrm{B}}_0 $$ field imperfections correction on BOLD sensitivity in 3D‐SPARKLING fMRI data. Magnetic Resonance in Medicine. 91(4). 1434–1448. 1 indexed citations
7.
Ciuciu, Philippe, et al.. (2023). Jointly Learning Non-Cartesian k-Space Trajectories and Reconstruction Networks for 2D and 3D MR Imaging through Projection. Bioengineering. 10(2). 158–158. 11 indexed citations
8.
Leroy, Claire, Sébastien Goutal, Philippe Gervais, et al.. (2023). A pharmacological imaging challenge based on 11C-buprenorphine PET-MRI to explore the response to opioids in humans. European Journal of Nuclear Medicine and Molecular Imaging. 50(10). 3153–3154. 1 indexed citations
9.
Chouzenoux, Émilie, et al.. (2021). Calibration-Less Multi-Coil Compressed Sensing Magnetic Resonance Image Reconstruction Based on OSCAR Regularization. Journal of Imaging. 7(3). 58–58. 4 indexed citations
10.
Ramzi, Zaccharie, Philippe Ciuciu, & Jean‐Luc Starck. (2020). Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets. Applied Sciences. 10(5). 1816–1816. 32 indexed citations
11.
Farrens, S., et al.. (2020). PySAP: Python Sparse Data Analysis Package for multidisciplinary image processing. Astronomy and Computing. 32. 100402–100402. 15 indexed citations
12.
Weiss, Pierre, Nicolas Chauffert, Franck Mauconduit, et al.. (2019). SPARKLING: variable‐density k‐space filling curves for accelerated T2*‐weighted MRI. Magnetic Resonance in Medicine. 81(6). 3643–3661. 50 indexed citations
13.
Fovet, Thomas, Tommy Löfstedt, Philippe Ciuciu, et al.. (2018). Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity. Human Brain Mapping. 39(4). 1777–1788. 13 indexed citations
14.
Löfstedt, Tommy, Charles Laidi, Julie Bourgin, et al.. (2018). Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity. Acta Psychiatrica Scandinavica. 138(6). 571–580. 16 indexed citations
15.
Weiss, Pierre, et al.. (2018). An empirical study of the maximum degree of undersampling in compressed sensing for T2*-weighted MRI. Magnetic Resonance Imaging. 53. 112–122. 3 indexed citations
16.
Rocca, Daria La, et al.. (2018). Self-similarity and multifractality in human brain activity: A wavelet-based analysis of scale-free brain dynamics. Journal of Neuroscience Methods. 309. 175–187. 32 indexed citations
17.
Chauffert, Nicolas, Pierre Weiss, Jonas Kahn, & Philippe Ciuciu. (2016). A Projection Algorithm for Gradient Waveforms Design in Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging. 35(9). 2026–2039. 19 indexed citations
18.
Chauffert, Nicolas, Philippe Ciuciu, Jonas Kahn, & Pierre Weiss. (2013). Variable density sampling with continuous sampling trajectories. arXiv (Cornell University). 3 indexed citations
19.
Chaâri, Lotfi, Sébastien Mériaux, Solveig Badillo, Jean‐Christophe Pesquet, & Philippe Ciuciu. (2012). Multidimensional Wavelet-based Regularized Reconstruction for Parallel Acquisition in Neuroimaging. arXiv (Cornell University). 5 indexed citations
20.
Chaâri, Lotfi, Jean‐Christophe Pesquet, Jean‐Yves Tourneret, Philippe Ciuciu, & Amel Benazza‐Benyahia. (2009). A Hierarchical Bayesian Model for Frame Representation. arXiv (Cornell University). 1 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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