Luyao Shi

1.3k total citations
40 papers, 937 citations indexed

About

Luyao Shi is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Luyao Shi has authored 40 papers receiving a total of 937 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Radiology, Nuclear Medicine and Imaging, 21 papers in Biomedical Engineering and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Luyao Shi's work include Medical Imaging Techniques and Applications (29 papers), Advanced X-ray and CT Imaging (21 papers) and Advanced MRI Techniques and Applications (11 papers). Luyao Shi is often cited by papers focused on Medical Imaging Techniques and Applications (29 papers), Advanced X-ray and CT Imaging (21 papers) and Advanced MRI Techniques and Applications (11 papers). Luyao Shi collaborates with scholars based in United States, China and France. Luyao Shi's co-authors include Huazhong Shu, Jean-Louis Coatrieux, Chi Liu, Yang Chen, John A. Onofrey, Jian Yang, Qianjing Feng, Limin Luo, Wufan Chen and Limin Luo and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Luyao Shi

35 papers receiving 927 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Luyao Shi United States 14 807 507 246 74 41 40 937
Dufan Wu United States 16 712 0.9× 463 0.9× 166 0.7× 102 1.4× 72 1.8× 59 901
Wenxiang Cong United States 17 761 0.9× 654 1.3× 169 0.7× 82 1.1× 68 1.7× 53 965
Qianjing Feng China 7 526 0.7× 392 0.8× 282 1.1× 30 0.4× 36 0.9× 11 729
Kyungsang Kim United States 17 1.1k 1.4× 632 1.2× 215 0.9× 210 2.8× 48 1.2× 55 1.3k
Daniel B. Russakoff United States 14 452 0.6× 163 0.3× 351 1.4× 69 0.9× 67 1.6× 38 822
Yongyi Shi United States 5 780 1.0× 566 1.1× 481 2.0× 80 1.1× 109 2.7× 22 1.1k
Yinsheng Li United States 14 480 0.6× 411 0.8× 117 0.5× 59 0.8× 33 0.8× 68 663
Zhaoying Bian China 19 1.3k 1.6× 1.0k 2.0× 265 1.1× 155 2.1× 43 1.0× 105 1.5k
Christine Toumoulin France 16 529 0.7× 357 0.7× 584 2.4× 32 0.4× 58 1.4× 67 984
Abolfazl Mehranian Switzerland 19 1.1k 1.3× 558 1.1× 79 0.3× 231 3.1× 26 0.6× 48 1.2k

Countries citing papers authored by Luyao Shi

Since Specialization
Citations

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

Fields of papers citing papers by Luyao Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luyao Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Luyao Shi. A scholar is included among the top collaborators of Luyao Shi 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 Luyao Shi. Luyao Shi 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
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3.
Mackin, Charles, et al.. (2024). Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization. 1–10. 2 indexed citations
4.
Vijayaraghavan, Prashanth, et al.. (2024). Self-Regulated Data-Free Knowledge Amalgamation for Text Classification. 491–502.
5.
Shi, Luyao, et al.. (2023). A Review of the Application of mNGS in Hematologic Malignancy. SHILAP Revista de lepidopterología. 4(1). 9–18.
6.
Guo, Xueqi, Luyao Shi, Xiongchao Chen, et al.. (2023). TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction. Lecture notes in computer science. 14288. 64–74. 2 indexed citations
7.
Toyonaga, Takuya, Luyao Shi, David Ménard, et al.. (2022). Deep learning–based attenuation correction for whole-body PET — a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. European Journal of Nuclear Medicine and Molecular Imaging. 49(9). 3086–3097. 12 indexed citations
8.
Shi, Luyao, et al.. (2022). Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application. Physics in Medicine and Biology. 68(3). 35014–35014. 14 indexed citations
9.
Chen, Xiongchao, Bo Zhou, Huidong Xie, et al.. (2022). Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT. European Journal of Nuclear Medicine and Molecular Imaging. 49(9). 3046–3060. 34 indexed citations
10.
Liu, Hui, Jing Wu, Luyao Shi, et al.. (2021). Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation. Journal of Nuclear Cardiology. 29(6). 2881–2892. 15 indexed citations
11.
Yang, Jaewon, Luyao Shi, Rui Wang, et al.. (2021). Direct image-based attenuation correction using conditional generative adversarial network for SPECT myocardial perfusion imaging. PubMed. 11600. 27–27. 15 indexed citations
12.
Yang, Jaewon, Luyao Shi, Rui Wang, et al.. (2021). Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study. Journal of Nuclear Medicine. 62(11). 1645–1652. 37 indexed citations
13.
Shi, Luyao, et al.. (2020). Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study. 743–754. 2 indexed citations
14.
Shi, Luyao, et al.. (2020). Deep learning-based attenuation map generation for myocardial perfusion SPECT. European Journal of Nuclear Medicine and Molecular Imaging. 47(10). 2383–2395. 78 indexed citations
15.
Chen, Yang, Jin Liu, Yining Hu, et al.. (2017). Discriminative feature representation: an effective postprocessing solution to low dose CT imaging. Physics in Medicine and Biology. 62(6). 2103–2131. 37 indexed citations
16.
You, Shoujiang, Luyao Shi, Chongke Zhong, et al.. (2016). Prognostic Significance of Estimated Glomerular Filtration Rate and Cystatin C in Patients with Acute Intracerebral Hemorrhage. Cerebrovascular Diseases. 42(5-6). 455–463. 11 indexed citations
17.
Shi, Luyao, Yining Hu, Yang Chen, et al.. (2016). Improving Low-dose Cardiac CT Images based on 3D Sparse Representation. Scientific Reports. 6(1). 22804–22804. 8 indexed citations
18.
Yang, Chen, Jian Yang, Huazhong Shu, et al.. (2014). 2-D Impulse Noise Suppression by Recursive Gaussian Maximum Likelihood Estimation. PLoS ONE. 9(5). e96386–e96386. 8 indexed citations
19.
Chen, Yang, Luyao Shi, Yang Jiang, et al.. (2014). Radiation dose reduction with dictionary learning based processing for head CT. Australasian Physical & Engineering Sciences in Medicine. 37(3). 483–493. 5 indexed citations
20.
Yang, Chen, Xindao Yin, Luyao Shi, et al.. (2013). Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing. Physics in Medicine and Biology. 58(16). 5803–5820. 167 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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