Sina Straub

1.2k total citations · 2 hit papers
33 papers, 777 citations indexed

About

Sina Straub is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology and Pathology and Forensic Medicine. According to data from OpenAlex, Sina Straub has authored 33 papers receiving a total of 777 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Neurology and 4 papers in Pathology and Forensic Medicine. Recurrent topics in Sina Straub's work include Advanced MRI Techniques and Applications (23 papers), Advanced Neuroimaging Techniques and Applications (12 papers) and MRI in cancer diagnosis (4 papers). Sina Straub is often cited by papers focused on Advanced MRI Techniques and Applications (23 papers), Advanced Neuroimaging Techniques and Applications (12 papers) and MRI in cancer diagnosis (4 papers). Sina Straub collaborates with scholars based in Germany, United States and Austria. Sina Straub's co-authors include Mark E. Ladd, Peter Bachert, Armin M. Nagel, Ewald Moser, Martin Meyerspeer, David G. Norris, Oliver Speck, Moritz Zaiß, Sebastian Schmitter and Frederik B. Laun and has published in prestigious journals such as NeuroImage, Journal of neurosurgery and Magnetic Resonance in Medicine.

In The Last Decade

Sina Straub

30 papers receiving 775 citations

Hit Papers

Pros and cons of ultra-high-field MRI/MRS for human appli... 2018 2026 2020 2023 2018 2024 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sina Straub Germany 12 564 119 106 101 98 33 777
Camilla Simmons United Kingdom 12 536 1.0× 63 0.5× 103 1.0× 147 1.5× 75 0.8× 23 930
Azma Mareyam United States 16 576 1.0× 167 1.4× 219 2.1× 123 1.2× 141 1.4× 30 826
Xiang He United States 16 975 1.7× 88 0.7× 81 0.8× 176 1.7× 145 1.5× 28 1.2k
Barbara Dymerska Austria 14 526 0.9× 64 0.5× 59 0.6× 131 1.3× 164 1.7× 28 726
Samuel Wharton United Kingdom 12 741 1.3× 92 0.8× 136 1.3× 184 1.8× 85 0.9× 25 946
Mélanie Schmitt Germany 16 610 1.1× 123 1.0× 79 0.7× 56 0.6× 181 1.8× 41 975
Marcel Warntjes Sweden 17 731 1.3× 49 0.4× 84 0.8× 102 1.0× 91 0.9× 39 984
Cynthia Wisnieff United States 8 949 1.7× 63 0.5× 170 1.6× 188 1.9× 121 1.2× 9 1.2k
Bryan Kressler United States 8 1.2k 2.1× 66 0.6× 136 1.3× 182 1.8× 155 1.6× 12 1.4k

Countries citing papers authored by Sina Straub

Since Specialization
Citations

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

Fields of papers citing papers by Sina Straub

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sina Straub

This figure shows the co-authorship network connecting the top 25 collaborators of Sina Straub. A scholar is included among the top collaborators of Sina Straub 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 Sina Straub. Sina Straub 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
2.
Middlebrooks, Erik H., Rémi Patriat, Jonathan C. Lau, et al.. (2025). Multi-institutional recommendations on the use of 7T MRI in deep brain stimulation. Journal of neurosurgery. 143(5). 1165–1175.
3.
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 →
4.
5.
Middlebrooks, Erik H., Vishal Patel, Sina Straub, et al.. (2024). 7 T Lesion-Attenuated Magnetization-Prepared Gradient Echo Acquisition for Detection of Posterior Fossa Demyelinating Lesions in Multiple Sclerosis. Investigative Radiology. 59(7). 513–518. 3 indexed citations
6.
Straub, Sina, et al.. (2024). <i>In-vitro</i> Detection of Intramammary-like Macrocalcifications Using Susceptibility-weighted MR Imaging Techniques at 1.5T. Magnetic Resonance in Medical Sciences. 24(4). n/a–n/a. 1 indexed citations
7.
Middlebrooks, Erik H., Philip W. Tipton, Elena Greco, et al.. (2024). Enhancing outcomes in deep brain stimulation: a comparative study of direct targeting using 7T versus 3T MRI. Journal of neurosurgery. 141(1). 252–259. 7 indexed citations
8.
Okromelidze, Lela, Vishal Patel, A. Sebastian López‐Chiriboga, et al.. (2023). Central Vein Sign in Multiple Sclerosis: A Comparison Study of the Diagnostic Performance of 3T versus 7T MRI. American Journal of Neuroradiology. 45(1). 76–81. 12 indexed citations
9.
Bilgic̦, Berkin, Mauro Costagli, Kwok‐Shing Chan, et al.. (2023). Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group.. PubMed. 3 indexed citations
10.
Dimov, Alexey, Thanh D. Nguyen, Pascal Spincemaille, et al.. (2023). QSM Throughout the Body. Journal of Magnetic Resonance Imaging. 57(6). 1621–1640. 13 indexed citations
13.
Bachert, Peter, et al.. (2021). A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry. Physica Medica. 88. 278–284. 4 indexed citations
14.
Schneider, Till, Jackie Ma, Patrick Wagner, et al.. (2021). Multiparametric MRI for Characterization of the Basal Ganglia and the Midbrain. Frontiers in Neuroscience. 15. 661504–661504. 4 indexed citations
15.
Bachert, Peter, et al.. (2020). On the influence of two coexisting species of susceptibility-producing structures on the R2∗ relaxation rate. Magnetic Resonance Imaging. 71. 170–177. 8 indexed citations
16.
Hoffmann, Alana, Lisa Seyler, Angelika Mennecke, et al.. (2020). Quantitative susceptibility mapping depicts severe myelin deficit and iron deposition in a transgenic model of multiple system atrophy. Experimental Neurology. 329. 113314–113314. 10 indexed citations
17.
Straub, Sina, Stephanie Mangesius, Elisabetta Indelicato, et al.. (2020). Toward quantitative neuroimaging biomarkers for Friedreich's ataxia at 7 Tesla: Susceptibility mapping, diffusion imaging, R 2 and R 1 relaxometry. Journal of Neuroscience Research. 98(11). 2219–2231. 8 indexed citations
18.
Ladd, Mark E., Peter Bachert, Martin Meyerspeer, et al.. (2018). Pros and cons of ultra-high-field MRI/MRS for human application. Progress in Nuclear Magnetic Resonance Spectroscopy. 109. 1–50. 368 indexed citations breakdown →
19.
Straub, Sina, Heinz‐Peter Schlemmer, Klaus Maier‐Hein, et al.. (2017). Mask-Adapted Background Field Removal for Artifact Reduction in Quantitative Susceptibility Mapping of the Prostate. Tomography. 3(2). 96–100. 7 indexed citations
20.
Straub, Sina, Frederik B. Laun, Björn Jobke, et al.. (2016). Potential of quantitative susceptibility mapping for detection of prostatic calcifications. Journal of Magnetic Resonance Imaging. 45(3). 889–898. 57 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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