Samuel A. Hurley

3.0k total citations
52 papers, 2.1k citations indexed

About

Samuel A. Hurley is a scholar working on Radiology, Nuclear Medicine and Imaging, Cognitive Neuroscience and Orthopedics and Sports Medicine. According to data from OpenAlex, Samuel A. Hurley has authored 52 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Cognitive Neuroscience and 5 papers in Orthopedics and Sports Medicine. Recurrent topics in Samuel A. Hurley's work include Advanced MRI Techniques and Applications (19 papers), Advanced Neuroimaging Techniques and Applications (16 papers) and Medical Imaging Techniques and Applications (13 papers). Samuel A. Hurley is often cited by papers focused on Advanced MRI Techniques and Applications (19 papers), Advanced Neuroimaging Techniques and Applications (16 papers) and Medical Imaging Techniques and Applications (13 papers). Samuel A. Hurley collaborates with scholars based in United States, United Kingdom and Italy. Samuel A. Hurley's co-authors include Ian J. Rowland, Jamison Grailer, Shaoqin Gong, Xiaoqiang Yang, Douglas A. Steeber, Andrew L. Alexander, Alexey Samsonov, Alireza Javadi, Aaron S. Field and Do Tromp and has published in prestigious journals such as Journal of Neuroscience, SHILAP Revista de lepidopterología and ACS Nano.

In The Last Decade

Samuel A. Hurley

50 papers receiving 2.1k citations

Peers

Samuel A. Hurley
Mohammad A. Yaseen United States
Pedro Ramos‐Cabrer Spain
Gao‐Jun Teng China
Jiun‐Jie Wang Taiwan
Florence Franconi France
P. J. Julyan United Kingdom
Youssef Zaim Wadghiri United States
Yinfeng Qian China
Bradford A. Moffat Australia
Xinqing Jiang China
Mohammad A. Yaseen United States
Samuel A. Hurley
Citations per year, relative to Samuel A. Hurley Samuel A. Hurley (= 1×) peers Mohammad A. Yaseen

Countries citing papers authored by Samuel A. Hurley

Since Specialization
Citations

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

Fields of papers citing papers by Samuel A. Hurley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Samuel A. Hurley. 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 Samuel A. Hurley. The network helps show where Samuel A. Hurley may publish in the future.

Co-authorship network of co-authors of Samuel A. Hurley

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel A. Hurley. A scholar is included among the top collaborators of Samuel A. Hurley 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 Samuel A. Hurley. Samuel A. Hurley 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.
2.
Wille, Christa M., et al.. (2024). Diffusion tensor imaging of hamstring muscles after acute strain injury and throughout recovery in collegiate athletes. Skeletal Radiology. 53(7). 1369–1379. 1 indexed citations
3.
Wille, Christa M., et al.. (2024). Association of quantitative diffusion tensor imaging measures with time to return to sport and reinjury incidence following acute hamstring strain injury. Journal of Biomechanics. 163. 111960–111960. 1 indexed citations
4.
Strigel, Roberta M., et al.. (2024). Attenuation correction and truncation completion for breast PET/MR imaging using deep learning. Physics in Medicine and Biology. 69(4). 45031–45031. 2 indexed citations
5.
Suminski, Aaron J., Rasmus M. Birn, Margaret E. Malone, et al.. (2023). Vagus nerve stimulation in the non-human primate: implantation methodology, characterization of nerve anatomy, target engagement and experimental applications. SHILAP Revista de lepidopterología. 9(1). 9–9. 4 indexed citations
6.
Osting, Susan, Samuel A. Hurley, Ajay Paul Singh, et al.. (2023). Quantifying changes in local basal ganglia structural connectivity in the 6-hydroxydopamine model of Parkinson's Disease using correlational tractography. PubMed. 2023. 1–4. 4 indexed citations
7.
Becker, K., Eduardo Aluicio‐Sarduy, Tyler Bradshaw, et al.. (2023). Cyclotron production of 43Sc and 44gSc from enriched 42CaO, 43CaO, and 44CaO targets. Frontiers in Chemistry. 11. 1167783–1167783. 14 indexed citations
8.
Kitzbichler, Manfred G., Gareth J. Barker, Tobias Wood, et al.. (2021). Peripheral inflammation is associated with micro-structural and functional connectivity changes in depression-related brain networks. Molecular Psychiatry. 26(12). 7346–7354. 52 indexed citations
9.
Juryńczyk, Maciej, Elzbieta Klimiec, Yazhuo Kong, et al.. (2021). Elucidating distinct clinico-radiologic signatures in the borderland between neuromyelitis optica and multiple sclerosis. Journal of Neurology. 269(1). 269–279. 6 indexed citations
10.
Deller, Timothy W., et al.. (2020). PET Image Quality Improvement for Simultaneous PET/MRI with a Lightweight MRI Surface Coil. Radiology. 298(1). 166–172. 8 indexed citations
11.
Collick, B., Samuel A. Hurley, Nader Behdad, et al.. (2020). Rapid development of application-specific flexible MRI receive coils. Physics in Medicine and Biology. 65(19). 19NT01–19NT01. 17 indexed citations
12.
Zhang, Yuxin, et al.. (2019). Detecting Microglial Density With Quantitative Multi-Compartment Diffusion MRI. Frontiers in Neuroscience. 13. 81–81. 71 indexed citations
13.
Dean, Douglas, Jitka Sojkova, Samuel A. Hurley, et al.. (2016). Alterations of Myelin Content in Parkinson’s Disease: A Cross-Sectional Neuroimaging Study. PLoS ONE. 11(10). e0163774–e0163774. 70 indexed citations
14.
Lewis, Christina, Samuel A. Hurley, M. Elizabeth Meyerand, & Cheng Guan Koay. (2015). Data‐driven optimized flip angle selection for T1 estimation from spoiled gradient echo acquisitions. Magnetic Resonance in Medicine. 76(3). 792–802. 6 indexed citations
15.
Emborg, Marina E., Samuel A. Hurley, Valerie Joers, et al.. (2014). Titer and Product Affect the Distribution of Gene Expression after Intraputaminal Convection-Enhanced Delivery. Stereotactic and Functional Neurosurgery. 92(3). 182–194. 15 indexed citations
16.
Bennett, Antonette, et al.. (2014). Differential effects of two MRI contrast agents on the integrity and distribution of rAAV2 and rAAV5 in the rat striatum. Molecular Therapy — Methods & Clinical Development. 1. 4–4. 4 indexed citations
17.
Hubbard, Paul, et al.. (2012). The familiar pattern of Chinese consumption growth. RePEc: Research Papers in Economics. 63–78. 4 indexed citations
18.
Alexander, Andrew L., Samuel A. Hurley, Alexey Samsonov, et al.. (2011). Characterization of Cerebral White Matter Properties Using Quantitative Magnetic Resonance Imaging Stains. Brain Connectivity. 1(6). 423–446. 355 indexed citations
19.
Yang, Xiaoqiang, Hao Hong, Jamison Grailer, et al.. (2011). cRGD-functionalized, DOX-conjugated, and 64Cu-labeled superparamagnetic iron oxide nanoparticles for targeted anticancer drug delivery and PET/MR imaging. Biomaterials. 32(17). 4151–4160. 336 indexed citations
20.
Yang, Xiaoqiang, Jamison Grailer, Ian J. Rowland, et al.. (2010). Multifunctional SPIO/DOX-loaded wormlike polymer vesicles for cancer therapy and MR imaging. Biomaterials. 31(34). 9065–9073. 180 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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