Muge Karaman

807 total citations
31 papers, 630 citations indexed

About

Muge Karaman is a scholar working on Radiology, Nuclear Medicine and Imaging, Nuclear and High Energy Physics and Neurology. According to data from OpenAlex, Muge Karaman has authored 31 papers receiving a total of 630 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Nuclear and High Energy Physics and 3 papers in Neurology. Recurrent topics in Muge Karaman's work include Advanced Neuroimaging Techniques and Applications (22 papers), MRI in cancer diagnosis (14 papers) and Advanced MRI Techniques and Applications (13 papers). Muge Karaman is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (22 papers), MRI in cancer diagnosis (14 papers) and Advanced MRI Techniques and Applications (13 papers). Muge Karaman collaborates with scholars based in United States, China and United Kingdom. Muge Karaman's co-authors include Xiaohong Joe Zhou, Yi Sui, Richard L. Magin, Ying Xiong, Jiaxuan Zhang, He Wang, Kejia Cai, Yuhua Li, Terri E. Weaver and Karen L. Xie and has published in prestigious journals such as Radiology, Magnetic Resonance in Medicine and Physics in Medicine and Biology.

In The Last Decade

Muge Karaman

31 papers receiving 624 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Muge Karaman United States 14 404 90 79 58 57 31 630
Qiuting Wen United States 14 366 0.9× 71 0.8× 68 0.9× 42 0.7× 85 1.5× 54 737
Mariko Goto Japan 15 501 1.2× 41 0.5× 17 0.2× 19 0.3× 69 1.2× 42 705
Ying Han United States 23 1.1k 2.8× 25 0.3× 62 0.8× 150 2.6× 132 2.3× 142 1.9k
Reed Selwyn United States 17 422 1.0× 62 0.7× 40 0.5× 12 0.2× 248 4.4× 29 973
Roberto Parodi Italy 15 160 0.4× 27 0.3× 44 0.6× 12 0.2× 76 1.3× 26 646
Jonathan D. Thiessen Canada 16 542 1.3× 72 0.8× 33 0.4× 6 0.1× 32 0.6× 72 817
Chad A. Holder United States 20 905 2.2× 59 0.7× 88 1.1× 56 1.0× 169 3.0× 42 1.3k
SriniVas R. Sadda United States 25 2.0k 5.0× 55 0.6× 15 0.2× 32 0.6× 133 2.3× 63 3.1k
Chang Yueh Ho United States 16 248 0.6× 65 0.7× 42 0.5× 27 0.5× 156 2.7× 47 786
Zhang Xue-lin China 17 347 0.9× 44 0.5× 214 2.7× 22 0.4× 64 1.1× 54 1.0k

Countries citing papers authored by Muge Karaman

Since Specialization
Citations

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

Fields of papers citing papers by Muge Karaman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Muge Karaman

This figure shows the co-authorship network connecting the top 25 collaborators of Muge Karaman. A scholar is included among the top collaborators of Muge Karaman 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 Muge Karaman. Muge Karaman 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.
Wang, Kezhou, et al.. (2023). Simultaneous multi-segment (SMSeg) EPI over multiple focal regions. Physics in Medicine and Biology. 68(4). 45001–45001. 2 indexed citations
2.
Mehta, Rahul, Changyu Zhou, Weihong Hu, et al.. (2023). Characterization of breast lesions using multi-parametric diffusion MRI and machine learning. Physics in Medicine and Biology. 68(8). 85006–85006. 1 indexed citations
3.
Karaman, Muge, et al.. (2022). Percentile-Based Analysis of Non-Gaussian Diffusion Parameters for Improved Glioma Grading. Investigative Magnetic Resonance Imaging. 26(2). 104–104. 1 indexed citations
4.
Karaman, Muge, et al.. (2021). Three‐dimensional reduced field‐of‐view imaging (3D‐rFOVI). Magnetic Resonance in Medicine. 87(5). 2372–2379. 6 indexed citations
5.
Wang, Yanchun, et al.. (2021). Evaluation of a fractional-order calculus diffusion model and bi-parametric VI-RADS for staging and grading bladder urothelial carcinoma. European Radiology. 32(2). 890–900. 32 indexed citations
6.
Karaman, Muge, Lei Tang, Ziyu Li, et al.. (2021). In vivo assessment of Lauren classification for gastric adenocarcinoma using diffusion MRI with a fractional order calculus model. European Radiology. 31(8). 5659–5668. 14 indexed citations
7.
Magin, Richard L., Matt G. Hall, Muge Karaman, & Viktor Vegh. (2020). Fractional Calculus Models of Magnetic Resonance Phenomena: Relaxation and Diffusion. Critical Reviews in Biomedical Engineering. 48(5). 285–326. 12 indexed citations
8.
An, Seungwon, Ilangovan Raju, Bayasgalan Surenkhuu, et al.. (2019). Neutrophil extracellular traps (NETs) contribute to pathological changes of ocular graft-vs.-host disease (oGVHD) dry eye: Implications for novel biomarkers and therapeutic strategies. The Ocular Surface. 17(3). 589–614. 69 indexed citations
9.
Gatto, Rodolfo G., Luis M. Colón-Pérez, Thomas H. Mareci, et al.. (2019). Detection of axonal degeneration in a mouse model of Huntington’s disease: comparison between diffusion tensor imaging and anomalous diffusion metrics. Magnetic Resonance Materials in Physics Biology and Medicine. 32(4). 461–471. 34 indexed citations
10.
Karaman, Muge, et al.. (2019). High-Spatial-Resolution Diffusion MRI in Parkinson Disease: Lateral Asymmetry of the Substantia Nigra. Radiology. 291(1). 149–157. 42 indexed citations
11.
Karaman, Muge & Xiaohong Joe Zhou. (2018). A fractional motion diffusion model for a twice‐refocused spin‐echo pulse sequence. NMR in Biomedicine. 31(11). e3960–e3960. 8 indexed citations
12.
Karaman, Muge, et al.. (2018). Quantitative MRI of Perivascular Spaces at 3T for Early Diagnosis of Mild Cognitive Impairment. American Journal of Neuroradiology. 39(9). 1622–1628. 30 indexed citations
13.
Magin, Richard L., Muge Karaman, Matt G. Hall, Wenzhen Zhu, & Xiaohong Joe Zhou. (2018). Capturing complexity of the diffusion-weighted MR signal decay. Magnetic Resonance Imaging. 56. 110–118. 13 indexed citations
14.
Xiong, Ying, Yi Sui, Zhipeng Xu, et al.. (2016). A Diffusion Tensor Imaging Study on White Matter Abnormalities in Patients with Type 2 Diabetes Using Tract-Based Spatial Statistics. American Journal of Neuroradiology. 37(8). 1462–1469. 50 indexed citations
15.
Sui, Yi, Ying Xiong, Jingjing Jiang, et al.. (2016). Differentiation of Low- and High-Grade Gliomas Using High b-Value Diffusion Imaging with a Non-Gaussian Diffusion Model. American Journal of Neuroradiology. 37(9). 1643–1649. 39 indexed citations
16.
Karaman, Muge, He Wang, Yi Sui, et al.. (2016). A fractional motion diffusion model for grading pediatric brain tumors. NeuroImage Clinical. 12. 707–714. 28 indexed citations
17.
Karaman, Muge, et al.. (2015). Incorporating relaxivities to more accurately reconstruct MR images. Magnetic Resonance Imaging. 33(4). 374–384. 5 indexed citations
18.
Karaman, Muge, et al.. (2014). Quantification of the Statistical Effects of Spatiotemporal Processing of Nontask fMRI Data. Brain Connectivity. 4(9). 649–661. 6 indexed citations
19.
Karaman, Muge, et al.. (2013). A statistical fMRI model for differential T2* contrast incorporating T1 and T2* of gray matter. Magnetic Resonance Imaging. 32(1). 9–27. 5 indexed citations
20.

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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