Adam Wang

2.3k total citations · 1 hit paper
128 papers, 1.6k citations indexed

About

Adam Wang is a scholar working on Biomedical Engineering, Radiology, Nuclear Medicine and Imaging and Radiation. According to data from OpenAlex, Adam Wang has authored 128 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 105 papers in Biomedical Engineering, 104 papers in Radiology, Nuclear Medicine and Imaging and 25 papers in Radiation. Recurrent topics in Adam Wang's work include Advanced X-ray and CT Imaging (91 papers), Medical Imaging Techniques and Applications (81 papers) and Radiation Dose and Imaging (64 papers). Adam Wang is often cited by papers focused on Advanced X-ray and CT Imaging (91 papers), Medical Imaging Techniques and Applications (81 papers) and Radiation Dose and Imaging (64 papers). Adam Wang collaborates with scholars based in United States, Germany and Switzerland. Adam Wang's co-authors include Norbert J. Pelc, Josh Star‐Lack, Jeffrey H. Siewerdsen, Yoshito Otake, Gerhard Kleinszig, J. Webster Stayman, Sebastian Vogt, A. Jay Khanna, J. E. Tkaczyk and Vladimir A. Lobastov and has published in prestigious journals such as Radiology, Spine and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

Adam Wang

121 papers receiving 1.6k citations

Hit Papers

Deep Learning Image Reconstruction for CT: Technical Prin... 2023 2026 2024 2025 2023 50 100 150
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. Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects
    2023 177 citations Adam Wang et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Adam Wang United States 22 1.2k 1.2k 370 232 127 128 1.6k
Daniel Kolditz Germany 15 1.0k 0.8× 954 0.8× 233 0.6× 254 1.1× 73 0.6× 30 1.4k
Bernhard Brendel Germany 20 1.1k 0.9× 958 0.8× 107 0.3× 120 0.5× 125 1.0× 53 1.4k
D.J. Hawkes United Kingdom 15 776 0.6× 703 0.6× 410 1.1× 222 1.0× 228 1.8× 42 1.7k
Wojciech Zbijewski United States 29 2.3k 1.8× 2.3k 1.9× 502 1.4× 367 1.6× 401 3.2× 178 3.1k
J. Anthony Seibert United States 29 1.7k 1.4× 1.2k 1.0× 306 0.8× 1.1k 4.6× 188 1.5× 70 2.3k
Yiannis Kyriakou Germany 20 1.1k 0.9× 1.0k 0.8× 247 0.7× 209 0.9× 61 0.5× 35 1.3k
Mohammad Reza Ay Iran 25 2.0k 1.6× 1.4k 1.1× 647 1.7× 387 1.7× 43 0.3× 182 2.4k
W Huda United States 20 1000 0.8× 635 0.5× 207 0.6× 328 1.4× 137 1.1× 75 1.5k
Jun Zhou United States 22 933 0.8× 463 0.4× 820 2.2× 751 3.2× 79 0.6× 121 1.6k
Sebastian Schäfer United States 21 630 0.5× 573 0.5× 227 0.6× 276 1.2× 291 2.3× 90 1.3k

Countries citing papers authored by Adam Wang

Since Specialization
Citations

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

Fields of papers citing papers by Adam Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Adam Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Adam Wang. A scholar is included among the top collaborators of Adam Wang 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 Adam Wang. Adam Wang 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, Sen, et al.. (2025). Unsupervised Training of a Dynamic Context-Aware Deep Denoising Framework for Low-Dose Fluoroscopic Imaging. IEEE Transactions on Instrumentation and Measurement. 74. 1–15. 1 indexed citations
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Wang, Sen, et al.. (2025). Multiple organ segmentation for CT scout images. 87–87.
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Wang, Adam, et al.. (2023). Introduction to Computation in Physical Sciences. 1 indexed citations
8.
Bennett, Roger, et al.. (2023). High Resolution Imaging with Focused kV X-Rays for Small Animal Radio-Neuromodulation. International Journal of Radiation Oncology*Biology*Physics. 117(2). e690–e691.
9.
Jacobson, Matthew W., et al.. (2021). Abbreviated on-treatment CBCT using roughness penalized mono-energization of kV-MV data and a multi-layer MV imager. Physics in Medicine and Biology. 66(13). 135001–135001. 4 indexed citations
10.
Shi, Linxi, Minghui Lu, N. Robert Bennett, et al.. (2020). Characterization and potential applications of a dual‐layer flat‐panel detector. Medical Physics. 47(8). 3332–3343. 45 indexed citations
11.
Shi, Linxi, N. Robert Bennett, Josh Star‐Lack, Minghui Lu, & Adam Wang. (2020). Projection-domain metal artifact correction using a dual layer detector. PubMed. 11312. 77–77. 6 indexed citations
12.
Star‐Lack, Josh, et al.. (2019). Scatter Correction for Industrial Cone-Beam Computed Tomography (CBCT) Using VSHARP, a fast GPU-Based Linear Boltzmann Transport Equation Solver. e-Journal of Nondestructive Testing. 24(3). 1 indexed citations
13.
Shi, Linxi, et al.. (2019). Fast shading correction for cone-beam CT via partitioned tissue classification. Physics in Medicine and Biology. 64(6). 65015–65015. 7 indexed citations
14.
Lu, Minghui, et al.. (2019). Dual energy imaging with a dual-layer flat panel detector. 40–40. 22 indexed citations
15.
Myronakis, Marios, Yue‐Houng Hu, Matthew W. Jacobson, et al.. (2019). A novel method for fast image simulation of flat panel detectors. Physics in Medicine and Biology. 64(9). 95019–95019. 13 indexed citations
16.
Star‐Lack, Josh, Adam Wang, George Zentai, et al.. (2015). A piecewise‐focused high DQE detector for MV imaging. Medical Physics. 42(9). 5084–5099. 31 indexed citations
17.
Zbijewski, Wojciech, Grace J. Gang, Jingyan Xu, et al.. (2014). Dual‐energy cone‐beam CT with a flat‐panel detector: Effect of reconstruction algorithm on material classification. Medical Physics. 41(2). 21908–21908. 32 indexed citations
18.
Dang, Hao, Adam Wang, Marc Sussman, J. H. Siewerdsen, & J. Webster Stayman. (2014). dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images. Physics in Medicine and Biology. 59(17). 4799–4826. 33 indexed citations
19.
Zbijewski, Wojciech, Grace J. Gang, Adam Wang, et al.. (2013). Noise reduction in material decomposition for low-dose dual-energy cone-beam CT. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8668. 866819–866819. 3 indexed citations
20.
Algozzine, Robert, Pam Campbell, & Adam Wang. (2009). 63 tactics for teaching diverse learners. Corwin eBooks. 2 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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