Karin Shmueli

4.0k total citations · 2 hit papers
65 papers, 3.0k citations indexed

About

Karin Shmueli is a scholar working on Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience and Neurology. According to data from OpenAlex, Karin Shmueli has authored 65 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Cognitive Neuroscience and 6 papers in Neurology. Recurrent topics in Karin Shmueli's work include Advanced MRI Techniques and Applications (45 papers), Advanced Neuroimaging Techniques and Applications (27 papers) and Functional Brain Connectivity Studies (14 papers). Karin Shmueli is often cited by papers focused on Advanced MRI Techniques and Applications (45 papers), Advanced Neuroimaging Techniques and Applications (27 papers) and Functional Brain Connectivity Studies (14 papers). Karin Shmueli collaborates with scholars based in United Kingdom, United States and Italy. Karin Shmueli's co-authors include Peter van Gelderen, Jeff H. Duyn, Jacco A. de Zwart, Masaki Fukunaga, Tie‐Qiang Li, Stephen Dodd, Silvina G. Horovitz, Jongho Lee, J. Martijn Jansma and Hellmut Merkle and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and NeuroImage.

In The Last Decade

Karin Shmueli

56 papers receiving 3.0k citations

Hit Papers

Magnetic susceptibility mapping of brain tissue in vivo u... 2009 2026 2014 2020 2009 2024 100 200 300 400

Peers

Karin Shmueli
Jongho Lee South Korea
José P. Marques Netherlands
Gunnar Krueger Switzerland
Markus Barth Australia
Yiping P. Du United States
Tobias Kober Switzerland
Karin Shmueli
Citations per year, relative to Karin Shmueli Karin Shmueli (= 1×) peers María A. Fernández‐Seara

Countries citing papers authored by Karin Shmueli

Since Specialization
Citations

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

Fields of papers citing papers by Karin Shmueli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Karin Shmueli

This figure shows the co-authorship network connecting the top 25 collaborators of Karin Shmueli. A scholar is included among the top collaborators of Karin Shmueli 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 Karin Shmueli. Karin Shmueli 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.
Stone, Alan J., et al.. (2025). Quantitative susceptibility mapping of the human carotid artery: Assessing sensitivity to elastin and collagen ex vivo. Magnetic Resonance in Medicine. 94(2). 771–784.
2.
Bhome, Rohan, George E. Thomas, Angeliki Zarkali, et al.. (2025). Quantitative susceptibility mapping reveals differences between subtypes of Lewy body dementia. Brain. 149(3). 1058–1069.
3.
Wastling, Stephen, et al.. (2024). Multiband accelerated 2D EPI for multi‐echo brain QSM at 3 T. Magnetic Resonance in Medicine. 93(1). 183–198.
4.
Thomas, George E., et al.. (2024). Longitudinal Associations of Magnetic Susceptibility with Clinical Severity in Parkinson's Disease. Movement Disorders. 39(3). 546–559. 14 indexed citations
5.
Bilgic̦, Berkin, Mauro Costagli, Kwok‐Shing Chan, et al.. (2024). Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro‐magnetic tissue properties study group. Magnetic Resonance in Medicine. 91(5). 1834–1862. 58 indexed citations breakdown →
6.
Chari, Ajai, Jan Sedlacik, Kiran Seunarine, et al.. (2023). Epileptogenic Tubers Are Associated with Increased Kurtosis of Susceptibility Values: A Combined Quantitative Susceptibility Mapping and Stereoelectroencephalography Pilot Study. American Journal of Neuroradiology. 44(8). 974–982. 1 indexed citations
7.
Kawadler, Jamie M., Karin Shmueli, Dawn E. Saunders, et al.. (2022). Brain volume in Tanzanian children with sickle cell anaemia: A neuroimaging study. British Journal of Haematology. 201(1). 114–124. 1 indexed citations
8.
Biondetti, Emma, Francesco Grussu, Marco Battiston, et al.. (2022). Multi‐echo quantitative susceptibility mapping: how to combine echoes for accuracy and precision at 3 Tesla. Magnetic Resonance in Medicine. 88(5). 2101–2116. 7 indexed citations
9.
Thornton, John S., et al.. (2021). The Effect of Oblique Image Slices on the Accuracy of Quantitative Susceptibility Mapping and a Robust Tilt Correction Method. UCL Discovery (University College London). 1 indexed citations
10.
Thomas, George E., Angeliki Zarkali, Mina Ryten, et al.. (2021). Regional brain iron and gene expression provide insights into neurodegeneration in Parkinson’s disease. Brain. 144(6). 1787–1798. 72 indexed citations
12.
Punwani, Shonit, et al.. (2019). High Repeatability of Quantitative Susceptibility Mapping (QSM) in the Head and Neck With a View to Detecting Hypoxic Cancer Sites. UCL Discovery (University College London). 1 indexed citations
13.
Biondetti, Emma, Magdalena Sokolska, Francesca B. Pizzini, et al.. (2019). Investigating the oxygenation of brain arteriovenous malformations using quantitative susceptibility mapping. NeuroImage. 199. 440–453. 12 indexed citations
14.
Punwani, Shonit, et al.. (2018). Fat Correction of MRI Phase Images for Accurate Susceptibility Mapping in the Head and Neck. UCL Discovery (University College London). 2 indexed citations
15.
Biondetti, Emma, David L. Thomas, & Karin Shmueli. (2016). Application of Laplacian-based Methods to Multi-echo Phase Data for Accurate Susceptibility Mapping. UCL Discovery (University College London). 7 indexed citations
16.
Shmueli, Karin, et al.. (2015). SAS: Symmetric Analysis of Z-Spectra, a Method to Evaluate B0 Correction Techniques for CEST Data in Clinical Systems Using Non-Exchanging Phantoms. UCL Discovery (University College London). 1 indexed citations
17.
Shmueli, Karin, et al.. (2010). Sensitivity of MRI resonance frequency to the orientation of brain tissue microstructure (vol 107, pg 5130, 2010). UCL Discovery (University College London). 7 indexed citations
18.
Shmueli, Karin, Jacco A. de Zwart, Peter van Gelderen, et al.. (2009). Magnetic susceptibility mapping of brain tissue in vivo using MRI phase data. Magnetic Resonance in Medicine. 62(6). 1510–1522. 437 indexed citations breakdown →
19.
Shmueli, Karin, Peter van Gelderen, Jacco A. de Zwart, et al.. (2007). Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal. NeuroImage. 38(2). 306–320. 439 indexed citations
20.
Vita, Enrico De, David L. Thomas, Harold G. Parkes, et al.. (2003). High resolution MRI of the brain at 4.7 Tesla using fast spin echo imaging. British Journal of Radiology. 76(909). 631–637. 46 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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