Simon Arberet

1.2k total citations
25 papers, 462 citations indexed

About

Simon Arberet is a scholar working on Signal Processing, Radiology, Nuclear Medicine and Imaging and Computational Mechanics. According to data from OpenAlex, Simon Arberet has authored 25 papers receiving a total of 462 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Signal Processing, 10 papers in Radiology, Nuclear Medicine and Imaging and 7 papers in Computational Mechanics. Recurrent topics in Simon Arberet's work include Blind Source Separation Techniques (11 papers), Advanced MRI Techniques and Applications (9 papers) and Speech and Audio Processing (8 papers). Simon Arberet is often cited by papers focused on Blind Source Separation Techniques (11 papers), Advanced MRI Techniques and Applications (9 papers) and Speech and Audio Processing (8 papers). Simon Arberet collaborates with scholars based in Switzerland, Germany and France. Simon Arberet's co-authors include Rémi Gribonval, Frédéric Bimbot, Dominik Nickel, Pierre Vandergheynst, Ahmed E. Othman, Saif Afat, Sebastian Gassenmaier, Judith Herrmann, Alexey Ozerov and Ngoc Q. K. Duong and has published in prestigious journals such as IEEE Transactions on Signal Processing, European Radiology and Signal Processing.

In The Last Decade

Simon Arberet

23 papers receiving 442 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Simon Arberet Switzerland 12 217 168 117 55 33 25 462
Thomas Goldstein United States 13 206 0.9× 19 0.1× 84 0.7× 76 1.4× 2 0.1× 19 452
András Zolnay Netherlands 15 160 0.7× 143 0.9× 9 0.1× 58 1.1× 18 0.5× 33 482
George Kontaxakis Spain 15 593 2.7× 10 0.1× 21 0.2× 186 3.4× 10 0.3× 64 735
Shuqian Luo China 12 289 1.3× 7 0.0× 20 0.2× 136 2.5× 14 0.4× 42 499
Changyu Sun United States 11 126 0.6× 37 0.2× 17 0.1× 20 0.4× 39 314
Suicheng Gu United States 14 151 0.7× 34 0.2× 7 0.1× 38 0.7× 3 0.1× 22 579
Pedro G. Vaz Portugal 9 121 0.6× 8 0.0× 33 0.3× 95 1.7× 2 0.1× 28 339
Wei Zha United States 12 131 0.6× 25 0.1× 27 0.2× 20 0.4× 29 376
Ukash Nakarmi United States 10 122 0.6× 15 0.1× 65 0.6× 51 0.9× 27 251
Karsten Østergaard Noe Denmark 9 227 1.0× 4 0.0× 18 0.2× 71 1.3× 9 0.3× 14 416

Countries citing papers authored by Simon Arberet

Since Specialization
Citations

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

Fields of papers citing papers by Simon Arberet

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon Arberet

This figure shows the co-authorship network connecting the top 25 collaborators of Simon Arberet. A scholar is included among the top collaborators of Simon Arberet 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 Simon Arberet. Simon Arberet 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.
Arberet, Simon, Ute Goerke, Kevin M. Johnson, et al.. (2024). Free-breathing T2 mapping of the abdomen in half the scan time using RADTSE with deep learning reconstruction. Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition.
2.
Hossu, Gabriela, Khalid Ambarki, Dominik Nickel, et al.. (2023). Deep learning HASTE sequence compared with T2-weighted BLADE sequence for liver MRI at 3 Tesla: a qualitative and quantitative prospective study. European Radiology. 33(10). 6817–6827. 11 indexed citations
3.
4.
Herrmann, Judith, Dominik Nickel, Simon Arberet, et al.. (2022). Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T. Academic Radiology. 30(1). 93–102. 11 indexed citations
5.
6.
Ginocchio, Luke, Paul Smereka, Angela Tong, et al.. (2022). Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection. Abdominal Radiology. 48(1). 282–290. 11 indexed citations
7.
8.
Arberet, Simon, Xiao Chen, Boris Mailhé, et al.. (2021). A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting. Magnetic Resonance Imaging. 82. 74–90.
9.
Herrmann, Judith, Sebastian Gassenmaier, Dominik Nickel, et al.. (2020). Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold. Investigative Radiology. 56(5). 313–319. 74 indexed citations
10.
Arberet, Simon, et al.. (2016). Centralized energy optimization at district level. 1–6. 1 indexed citations
11.
Renevey, Philippe, P. Celka, Simon Arberet, et al.. (2014). Photoplethysmography-based Bracelet for Automatic Sleep Stages Classification: Preliminary Results. 1 indexed citations
12.
Arberet, Simon & Pierre Vandergheynst. (2014). Reverberant Audio Source Separation via Sparse and Low-Rank Modeling. IEEE Signal Processing Letters. 21(4). 404–408. 6 indexed citations
13.
Arberet, Simon, Mathieu Lemay, Philippe Renevey, et al.. (2013). Photoplethysmography-based ambulatory heartbeat monitoring embedded into a dedicated bracelet. 935–938. 22 indexed citations
14.
Arberet, Simon, Alexey Ozerov, Frédéric Bimbot, & Rémi Gribonval. (2012). A tractable framework for estimating and combining spectral source models for audio source separation. Signal Processing. 92(8). 1886–1901. 6 indexed citations
15.
Arberet, Simon, et al.. (2011). A wideband doubly-sparse approach for MITO sparse filter estimation. HAL (Le Centre pour la Communication Scientifique Directe). 53. 2876–2879. 2 indexed citations
16.
Golbabaee, Mohammad, Simon Arberet, & Pierre Vandergheynst. (2010). Multichannel compressed sensing via source separation for hyperspectral images. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1326–1329. 8 indexed citations
17.
Arberet, Simon, Alexey Ozerov, Ngoc Q. K. Duong, et al.. (2010). Nonnegative matrix factorization and spatial covariance model for under-determined reverberant audio source separation. HAL (Le Centre pour la Communication Scientifique Directe). 1–4. 61 indexed citations
18.
Arberet, Simon. (2010). Hyper-DEMIX: Blind source separation of hyperspectral images using local ML estimates. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1393–1396. 1 indexed citations
19.
Arberet, Simon, Rémi Gribonval, & Frédéric Bimbot. (2009). A Robust Method to Count and Locate Audio Sources in a Multichannel Underdetermined Mixture. IEEE Transactions on Signal Processing. 58(1). 121–133. 68 indexed citations
20.
Arberet, Simon, Rémi Gribonval, & Frédéric Bimbot. (2008). A robust method to count, locate and separate audio sources in a multichannel underdetermined mixture. HAL (Le Centre pour la Communication Scientifique Directe). 29. 3 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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