Lijun Lu

1.8k total citations
83 papers, 1.3k citations indexed

About

Lijun Lu is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Lijun Lu has authored 83 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Radiology, Nuclear Medicine and Imaging, 31 papers in Biomedical Engineering and 15 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Lijun Lu's work include Radiomics and Machine Learning in Medical Imaging (49 papers), Medical Imaging Techniques and Applications (41 papers) and Advanced X-ray and CT Imaging (26 papers). Lijun Lu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (49 papers), Medical Imaging Techniques and Applications (41 papers) and Advanced X-ray and CT Imaging (26 papers). Lijun Lu collaborates with scholars based in China, United States and Canada. Lijun Lu's co-authors include Arman Rahmim, Wenbing Lv, Jianhua Ma, Wufan Chen, Qianjin Feng, Qingyu Yuan, Quanshi Wang, Saeed Ashrafinia, Wei Yang and Jun Jiang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and IEEE Transactions on Medical Imaging.

In The Last Decade

Lijun Lu

76 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lijun Lu China 20 1.1k 394 358 155 149 83 1.3k
Ida Häggström United States 9 1.2k 1.1× 353 0.9× 320 0.9× 181 1.2× 145 1.0× 20 1.4k
Wenbing Lv China 15 874 0.8× 250 0.6× 424 1.2× 183 1.2× 126 0.8× 44 1.0k
Sylvain Reuzé France 12 1.7k 1.6× 436 1.1× 565 1.6× 372 2.4× 232 1.6× 18 2.0k
Sibo Tian United States 23 767 0.7× 362 0.9× 596 1.7× 240 1.5× 157 1.1× 107 1.7k
Jifke F. Veenland Netherlands 22 733 0.7× 342 0.9× 418 1.2× 91 0.6× 173 1.2× 51 1.4k
Maria Thor United States 23 922 0.8× 252 0.6× 650 1.8× 211 1.4× 80 0.5× 97 1.4k
Wouter J. C. van Elmpt Netherlands 16 1.2k 1.1× 399 1.0× 642 1.8× 190 1.2× 143 1.0× 22 1.5k
Bal Sanghera United Kingdom 15 904 0.8× 239 0.6× 306 0.9× 246 1.6× 126 0.8× 44 1.3k
Tyler Bradshaw United States 19 1.1k 1.0× 372 0.9× 276 0.8× 125 0.8× 119 0.8× 63 1.4k
J. Castelli France 24 924 0.8× 229 0.6× 697 1.9× 208 1.3× 85 0.6× 99 1.7k

Countries citing papers authored by Lijun Lu

Since Specialization
Citations

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

Fields of papers citing papers by Lijun Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lijun Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Lijun Lu. A scholar is included among the top collaborators of Lijun Lu 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 Lijun Lu. Lijun Lu 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.
Chen, Zihang, Xu Han, Li Lin, et al.. (2025). Harnessing deep learning to optimize induction chemotherapy choices in nasopharyngeal carcinoma. Radiotherapy and Oncology. 211. 111047–111047.
2.
Lv, Wenbing, Chen‐Fei Wu, Zhilong Chen, et al.. (2025). A Serial MRI–based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma. Radiology Artificial Intelligence. 7(2). e230544–e230544.
3.
4.
Salimi, Yazdan, Wenbing Lv, Hongwen Chen, et al.. (2024). Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET. EJNMMI Physics. 11(1). 66–66.
5.
Lin, Guoyu, et al.. (2024). Semi-supervised model based on implicit neural representation and mutual learning (SIMN) for multi-center nasopharyngeal carcinoma segmentation on MRI. Computers in Biology and Medicine. 175. 108368–108368. 3 indexed citations
6.
Zhao, Tianyun, et al.. (2024). Associating Knee Osteoarthritis Progression with Temporal‐Regional Graph Convolutional Network Analysis on MR Images. Journal of Magnetic Resonance Imaging. 61(1). 378–391. 3 indexed citations
7.
Peng, Lihong, et al.. (2024). BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis. Physics in Medicine and Biology. 69(10). 105007–105007. 2 indexed citations
8.
Xu, Hui, Wenbing Lv, Hao Zhang, et al.. (2023). Multimodality radiomics analysis based on [18F]FDG PET/CT imaging and multisequence MRI: application to nasopharyngeal carcinoma prognosis. European Radiology. 33(10). 6677–6688. 6 indexed citations
9.
Liu, Jinghua, Xiaolei Zhang, Zhongxiao Wang, et al.. (2023). Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer. EJNMMI Research. 13(1). 6 indexed citations
10.
Wu, Huiqin, Xiaohui Liu, Lihong Peng, et al.. (2023). Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer. Physics in Medicine and Biology. 68(22). 225014–225014. 2 indexed citations
11.
Lin, Guoyu, Yiwen Zhang, Jian Geng, et al.. (2023). GCLR: A self-supervised representation learning pretext task for glomerular filtration barrier segmentation in TEM images. Artificial Intelligence in Medicine. 146. 102720–102720. 4 indexed citations
12.
Peng, Lihong, Hui Xu, Wenbing Lv, Lijun Lu, & Wufan Chen. (2023). Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT. Cancers. 15(3). 932–932. 2 indexed citations
13.
14.
Lv, Wenbing, et al.. (2022). Deep learning–based harmonization of CT reconstruction kernels towards improved clinical task performance. European Radiology. 33(4). 2426–2438. 6 indexed citations
15.
Gao, Yuanyuan, Yansong Zhu, Murat Bilgel, et al.. (2021). Voxel-based partial volume correction of PET images via subtle MRI guided non-local means regularization. Physica Medica. 89. 129–139. 11 indexed citations
16.
Dong, Fang, Yu Jiang, Lijun Lu, et al.. (2019). Construction of prognostic microRNA signature for human invasive breast cancer by integrated analysis. SHILAP Revista de lepidopterología. 1 indexed citations
17.
Tang, Jing, Bao Yang, Ivan S. Klyuzhin, et al.. (2019). Artificial Neural Network–Based Prediction of Outcome in Parkinson’s Disease Patients Using DaTscan SPECT Imaging Features. Molecular Imaging and Biology. 21(6). 1165–1173. 40 indexed citations
18.
Zeng, Dong, Zhaoying Bian, Jing Huang, et al.. (2016). Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study. Physics in Medicine and Biology. 61(22). 8135–8156. 12 indexed citations
19.
Zeng, Dong, Xinyu Zhang, Zhaoying Bian, et al.. (2016). Cerebral perfusion computed tomography deconvolution via structure tensor total variation regularization. Medical Physics. 43(5). 2091–2107. 27 indexed citations
20.
Lu, Lijun, Nicolas A. Karakatsanis, Jing Tang, Wufan Chen, & Arman Rahmim. (2012). 3.5D dynamic PET image reconstruction incorporating kinetics-based clusters. Physics in Medicine and Biology. 57(15). 5035–5055. 29 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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