Yu Shang

2.1k total citations
100 papers, 1.7k citations indexed

About

Yu Shang is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Surgery. According to data from OpenAlex, Yu Shang has authored 100 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Radiology, Nuclear Medicine and Imaging, 61 papers in Biomedical Engineering and 24 papers in Surgery. Recurrent topics in Yu Shang's work include Optical Imaging and Spectroscopy Techniques (59 papers), Photoacoustic and Ultrasonic Imaging (35 papers) and Non-Invasive Vital Sign Monitoring (23 papers). Yu Shang is often cited by papers focused on Optical Imaging and Spectroscopy Techniques (59 papers), Photoacoustic and Ultrasonic Imaging (35 papers) and Non-Invasive Vital Sign Monitoring (23 papers). Yu Shang collaborates with scholars based in China, United States and Spain. Yu Shang's co-authors include Guoqiang Yu, Ran Cheng, Lixin Dong, Yu Lin, Daniel Irwin, Lian He, Sibu P. Saha, Chong Huang, Youquan Zhao and Mahesh Kudrimoti and has published in prestigious journals such as Applied Physics Letters, NeuroImage and Scientific Reports.

In The Last Decade

Yu Shang

87 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yu Shang China 26 1.3k 961 368 366 191 100 1.7k
Roman Maniewski Poland 21 836 0.7× 588 0.6× 169 0.5× 228 0.6× 405 2.1× 112 1.6k
David R. Busch United States 22 966 0.8× 790 0.8× 269 0.7× 100 0.3× 121 0.6× 71 1.5k
Khosrow Behbehani United States 20 262 0.2× 448 0.5× 147 0.4× 304 0.8× 419 2.2× 96 1.3k
Ashwin B. Parthasarathy United States 17 1.1k 0.9× 617 0.6× 166 0.5× 480 1.3× 89 0.5× 66 1.5k
Hebe Désirée Kvernmo Norway 18 670 0.5× 321 0.3× 432 1.2× 887 2.4× 666 3.5× 26 1.7k
Alan Bernjak United Kingdom 19 482 0.4× 365 0.4× 245 0.7× 522 1.4× 740 3.9× 36 1.6k
E. Göran Salerud Sweden 15 319 0.3× 197 0.2× 280 0.8× 496 1.4× 199 1.0× 29 1.0k
Joyce M. Evans United States 27 279 0.2× 291 0.3× 560 1.5× 497 1.4× 820 4.3× 82 2.0k
Vladimír Blažek Germany 22 439 0.3× 1.1k 1.1× 595 1.6× 187 0.5× 538 2.8× 97 1.6k
Eveline Huber United Kingdom 15 275 0.2× 254 0.3× 177 0.5× 132 0.4× 26 0.1× 24 996

Countries citing papers authored by Yu Shang

Since Specialization
Citations

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

Fields of papers citing papers by Yu Shang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Shang

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Shang. A scholar is included among the top collaborators of Yu Shang 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 Yu Shang. Yu Shang 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, Jihui, et al.. (2025). A deep convolutional neural network for diffuse correlation tomography. Applied Physics Letters. 126(8). 1 indexed citations
2.
Shang, Yu, et al.. (2025). Common biomarkers of idiopathic pulmonary fibrosis and systemic sclerosis based on WGCNA and machine learning. Scientific Reports. 15(1). 610–610. 2 indexed citations
4.
Shang, Yu, et al.. (2024). A crossed T-gradient metamaterial for enhanced acoustic sensing. Applied Acoustics. 227. 110209–110209. 5 indexed citations
5.
Shang, Yu, et al.. (2024). An extensive upgrading of contact diffuse CorrelationTomography system. International Journal of Imaging Systems and Technology. 34(3). 1 indexed citations
6.
Zhang, Ruizhi, et al.. (2024). Diffuse correlation tomography: a technique to characterize tissue blood flow abnormalities in benign and malignant breast lesions. Biomedical Optics Express. 15(11). 6259–6259. 1 indexed citations
7.
Shang, Yu, et al.. (2023). Three-Dimensional Gradient Metamaterial Devices Coupled with Phononic Crystals for Acoustic Enhancement Sensing. Crystals. 13(8). 1191–1191. 8 indexed citations
8.
Li, Weilong, Zihao Zhang, Zhiyi Li, Zhiguo Gui, & Yu Shang. (2023). Correlation and asynchronization of electroencephalogram and cerebral blood flow in active and passive stimulations. Journal of Neural Engineering. 20(6). 66007–66007.
9.
Jiang, Wencai, Meixiang Chen, Jianyu Huang, et al.. (2021). Proteinuria is independently associated with carotid atherosclerosis: a multicentric study. BMC Cardiovascular Disorders. 21(1). 554–554. 2 indexed citations
10.
Li, Yupeng, Gangao Wu, Yu Shang, et al.. (2020). ILDGDB: a manually curated database of genomics, transcriptomics, proteomics and drug information for interstitial lung diseases. BMC Pulmonary Medicine. 20(1). 323–323. 3 indexed citations
11.
Zhao, Yuan, et al.. (2019). The influence of morphological distribution of melanin on parameter selection in laser thermotherapy for vascular skin diseases. Lasers in Medical Science. 35(4). 901–917. 1 indexed citations
12.
Gui, Zhiguo, et al.. (2018). Approaches to denoise the diffuse optical signals for tissue blood flow measurement. Biomedical Optics Express. 9(12). 6170–6170. 19 indexed citations
13.
Luo, Shitong, et al.. (2017). Validation of Material Algorithms for Femur Remodelling Using Medical Image Data. Applied Bionics and Biomechanics. 2017. 1–10. 2 indexed citations
14.
Shang, Yu, et al.. (2017). Clinical applications of near-infrared diffuse correlation spectroscopy and tomography for tissue blood flow monitoring and imaging. Physiological Measurement. 38(4). R1–R26. 61 indexed citations
15.
Li, Ting, Yu Lin, Yu Shang, et al.. (2013). Simultaneous measurement of deep tissue blood flow and oxygenation using noncontact diffuse correlation spectroscopy flow-oximeter. Scientific Reports. 3(1). 1358–1358. 85 indexed citations
16.
He, Lian, Yu Lin, Yu Shang, Brent J. Shelton, & Guoqiang Yu. (2013). Using optical fibers with different modes to improve the signal-to-noise ratio of diffuse correlation spectroscopy flow-oximeter measurements. Journal of Biomedical Optics. 18(3). 37001–37001. 37 indexed citations
17.
Dong, Lixin, Lian He, Yu Lin, Yu Shang, & Guoqiang Yu. (2012). Simultaneously Extracting Multiple Parameters via Fitting One Single Autocorrelation Function Curve in Diffuse Correlation Spectroscopy. IEEE Transactions on Biomedical Engineering. 60(2). 361–368. 37 indexed citations
18.
Cheng, Ran, Yu Shang, Don Hayes, Sibu P. Saha, & Guoqiang Yu. (2012). Noninvasive optical evaluation of spontaneous low frequency oscillations in cerebral hemodynamics. NeuroImage. 62(3). 1445–1454. 86 indexed citations
19.
Yu, Guoqiang, Yu Shang, Youquan Zhao, et al.. (2011). Intraoperative evaluation of revascularization effect on ischemic muscle hemodynamics using near-infrared diffuse optical spectroscopies. Journal of Biomedical Optics. 16(2). 27004–27004. 43 indexed citations
20.
Shang, Yu, Lei Chen, Michał Toborek, & Guoqiang Yu. (2011). Diffuse optical monitoring of repeated cerebral ischemia in mice. Optics Express. 19(21). 20301–20301. 63 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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