Pu‐Yeh Wu

1.6k total citations · 1 hit paper
60 papers, 1.0k citations indexed

About

Pu‐Yeh Wu is a scholar working on Radiology, Nuclear Medicine and Imaging, Neurology and Epidemiology. According to data from OpenAlex, Pu‐Yeh Wu has authored 60 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Radiology, Nuclear Medicine and Imaging, 13 papers in Neurology and 11 papers in Epidemiology. Recurrent topics in Pu‐Yeh Wu's work include Radiomics and Machine Learning in Medical Imaging (15 papers), MRI in cancer diagnosis (10 papers) and Advanced MRI Techniques and Applications (10 papers). Pu‐Yeh Wu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (15 papers), MRI in cancer diagnosis (10 papers) and Advanced MRI Techniques and Applications (10 papers). Pu‐Yeh Wu collaborates with scholars based in China, Taiwan and Spain. Pu‐Yeh Wu's co-authors include Bo Yin, Xuanxuan Li, Dongdong Wang, Yajing Zhao, Yiping Lu, Chu‐Chung Huang, Tianye Jia, Nan Mei, Anling Xiao and Daoying Geng and has published in prestigious journals such as Journal of Neuroscience, SHILAP Revista de lepidopterología and NeuroImage.

In The Last Decade

Pu‐Yeh Wu

54 papers receiving 1.0k citations

Hit Papers

Cerebral Micro-Structural Changes in COVID-19 Patients – ... 2020 2026 2022 2024 2020 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Cerebral Micro-Structural Changes in COVID-19 Patients – An MRI-based 3-month Follow-up Study
    2020 417 citations Yiping Lu, Xuanxuan Li et al. EClinicalMedicine profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pu‐Yeh Wu China 16 419 343 126 120 115 60 1.0k
Aurélie Kas France 19 551 1.3× 184 0.5× 87 0.7× 57 0.5× 297 2.6× 73 1.2k
Karim Farid France 16 421 1.0× 115 0.3× 32 0.3× 62 0.5× 66 0.6× 46 969
Joel Simrén Sweden 21 514 1.2× 74 0.2× 94 0.7× 53 0.4× 80 0.7× 44 1.6k
Tobias Granberg Sweden 26 839 2.0× 648 1.9× 138 1.1× 34 0.3× 139 1.2× 84 2.2k
Stefano Gipponi Italy 15 322 0.8× 89 0.3× 33 0.3× 74 0.6× 75 0.7× 33 644
Fajin Lv China 19 401 1.0× 431 1.3× 36 0.3× 22 0.2× 281 2.4× 89 1.2k
Thomas Reithmeier Germany 19 478 1.1× 218 0.6× 38 0.3× 23 0.2× 135 1.2× 36 1.4k
Fabrizio Cocciolillo Italy 18 271 0.6× 133 0.4× 17 0.1× 98 0.8× 111 1.0× 38 806
Matteo Foschi Italy 11 484 1.2× 37 0.1× 117 0.9× 37 0.3× 49 0.4× 55 1.0k
György A. Homola Germany 17 192 0.5× 158 0.5× 18 0.1× 24 0.2× 191 1.7× 46 868

Countries citing papers authored by Pu‐Yeh Wu

Since Specialization
Citations

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

Fields of papers citing papers by Pu‐Yeh Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pu‐Yeh Wu. 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 Pu‐Yeh Wu. The network helps show where Pu‐Yeh Wu may publish in the future.

Co-authorship network of co-authors of Pu‐Yeh Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Pu‐Yeh Wu. A scholar is included among the top collaborators of Pu‐Yeh Wu 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 Pu‐Yeh Wu. Pu‐Yeh Wu 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, Dan, Huan Zhang, Xiaowen Ma, et al.. (2025). A multifunctional Fe/Mn Co-doped MXene nanocomposite with MR imaging and photothermal for anti-cancer therapy. Materials Today Chemistry. 46. 102739–102739. 1 indexed citations
2.
3.
Chen, Jie, Nan Mei, Xuanxuan Li, et al.. (2024). Assessment of hypoxia and its dynamic evolution in glioblastoma via qBOLD MRI: a comparative study with metformin treatment. European Radiology Experimental. 8(1). 134–134. 1 indexed citations
4.
Zeng, Fang, Xiaoxue Tang, Lin Lin, et al.. (2024). Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer. European Journal of Radiology. 172. 111325–111325. 2 indexed citations
5.
Wu, Shiman, Tingting Xu, Jiahao Gao, et al.. (2024). Non-invasive diagnosis of liver fibrosis via MRI using targeted gadolinium-based nanoparticles. European Journal of Nuclear Medicine and Molecular Imaging. 52(1). 48–61. 5 indexed citations
6.
Zheng, Tingting, et al.. (2024). Prediction model based on MRI morphological features for distinguishing benign and malignant thyroid nodules. BMC Cancer. 24(1). 256–256. 1 indexed citations
7.
Hu, Wenjuan, et al.. (2023). MRI-Based Texture Analysis for Preoperative Prediction of BRAF V600E Mutation in Papillary Thyroid Carcinoma. Journal of Multidisciplinary Healthcare. Volume 16. 1–10. 4 indexed citations
8.
Wang, Yanwei, et al.. (2023). Study of T2 mapping in quantifying and discriminating uterine lesions under different magnetic field strengths: 1.5 T vs. 3.0 T. BMC Medical Imaging. 23(1). 1–1. 8 indexed citations
9.
Wu, Pu‐Yeh, et al.. (2023). Altered functional brain networks in coronary heart disease: independent component analysis and graph theoretical analysis. Brain Structure and Function. 229(1). 133–142. 2 indexed citations
10.
Zhu, Jie, Jun Du, Pu‐Yeh Wu, et al.. (2023). Diffusion-weighted, intravoxel incoherent motion, and diffusion kurtosis tensor MR imaging in chronic kidney diseases: Correlations with histology. Magnetic Resonance Imaging. 106. 1–7. 15 indexed citations
11.
12.
Wang, Bin, Yongfang Wang, Yan Tan, et al.. (2022). Assessment of Fasudil on Contrast-Associated Acute Kidney Injury Using Multiparametric Renal MRI. Frontiers in Pharmacology. 13. 905547–905547. 6 indexed citations
13.
Wang, Dongdong, Yiping Lu, Xuanxuan Li, et al.. (2022). Evaluation of HIF-1α Expression in a Rat Glioma Model Using Intravoxel Incoherent Motion and R2* Mapping. Frontiers in Oncology. 12. 902612–902612. 5 indexed citations
14.
15.
Li, Qin, Qin Xiao, Yang Meng, et al.. (2021). Histogram analysis of quantitative parameters from synthetic MRI: Correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer. European Journal of Radiology. 139. 109697–109697. 28 indexed citations
16.
Lu, Yiping, Xuanxuan Li, Daoying Geng, et al.. (2020). Cerebral Micro-Structural Changes in COVID-19 Patients – An MRI-based 3-month Follow-up Study. EClinicalMedicine. 25. 100484–100484. 417 indexed citations breakdown →
17.
Li, Shuhua, Piu Chan, Chunmei Li, et al.. (2020). Changes of Amide Proton Transfer Imaging in Multiple System Atrophy Parkinsonism Type. Frontiers in Aging Neuroscience. 12. 572421–572421. 3 indexed citations
18.
19.
Hu, Wenjuan, Ran Wei, Shaofeng Duan, et al.. (2020). MRI-based radiomics analysis to predict preoperative lymph node metastasis in papillary thyroid carcinoma. Gland Surgery. 9(5). 1214–1226. 30 indexed citations
20.
Chiang, Po‐Han, Pu‐Yeh Wu, Yu‐Chao Liu, et al.. (2012). GABA Is Depolarizing in Hippocampal Dentate Granule Cells of the Adolescent and Adult Rats. Journal of Neuroscience. 32(1). 62–67. 48 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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