Grace J. Gang

1.5k total citations
100 papers, 1.1k citations indexed

About

Grace J. Gang is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Grace J. Gang has authored 100 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 96 papers in Radiology, Nuclear Medicine and Imaging, 82 papers in Biomedical Engineering and 15 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Grace J. Gang's work include Advanced X-ray and CT Imaging (81 papers), Medical Imaging Techniques and Applications (80 papers) and Radiation Dose and Imaging (61 papers). Grace J. Gang is often cited by papers focused on Advanced X-ray and CT Imaging (81 papers), Medical Imaging Techniques and Applications (80 papers) and Radiation Dose and Imaging (61 papers). Grace J. Gang collaborates with scholars based in United States, Germany and Canada. Grace J. Gang's co-authors include J. Webster Stayman, Jeffrey H. Siewerdsen, Wojciech Zbijewski, J. H. Siewerdsen, Daniel J. Tward, Junghoon Lee, J. H. Siewerdsen, Tina Ehtiati, Jerry L. Prince and John A. Carrino and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging.

In The Last Decade

Grace J. Gang

98 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Grace J. Gang United States 17 991 914 264 152 59 100 1.1k
Pascal Monnin Switzerland 18 667 0.7× 578 0.6× 355 1.3× 106 0.7× 98 1.7× 44 869
Taly Gilat Schmidt United States 16 901 0.9× 868 0.9× 130 0.5× 93 0.6× 22 0.4× 82 1.0k
Katsuhiro Ichikawa Japan 15 629 0.6× 523 0.6× 234 0.9× 85 0.6× 14 0.2× 108 785
Alexander L. C. Kwan United States 12 957 1.0× 677 0.7× 670 2.5× 190 1.3× 146 2.5× 24 1.1k
Gregory J. Michalak United States 17 533 0.5× 529 0.6× 141 0.5× 82 0.5× 23 0.4× 34 771
Lars Gjesteby United States 12 619 0.6× 562 0.6× 65 0.2× 98 0.6× 46 0.8× 31 868
Kirsten Boedeker United States 10 804 0.8× 730 0.8× 291 1.1× 122 0.8× 11 0.2× 28 1.0k
Marcel Beister Germany 6 740 0.7× 661 0.7× 125 0.5× 109 0.7× 11 0.2× 17 845
Lars Gunnar Månsson Sweden 19 895 0.9× 522 0.6× 740 2.8× 43 0.3× 64 1.1× 57 1.0k
Bernhard Renger Germany 13 513 0.5× 377 0.4× 140 0.5× 55 0.4× 28 0.5× 41 603

Countries citing papers authored by Grace J. Gang

Since Specialization
Citations

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

Fields of papers citing papers by Grace J. Gang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Grace J. Gang. 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 Grace J. Gang. The network helps show where Grace J. Gang may publish in the future.

Co-authorship network of co-authors of Grace J. Gang

This figure shows the co-authorship network connecting the top 25 collaborators of Grace J. Gang. A scholar is included among the top collaborators of Grace J. Gang 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 Grace J. Gang. Grace J. Gang 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.
Boedeker, Kirsten, et al.. (2025). Fourier Diffusion Models: A Method to Control MTF and NPS in Score-Based Stochastic Image Generation. IEEE Transactions on Medical Imaging. 44(9). 3694–3704. 1 indexed citations
2.
Mei, Kai, Nadav Shapira, J. Webster Stayman, et al.. (2024). 3D printed phantom with 12 000 submillimeter lesions to improve efficiency in CT detectability assessment. Medical Physics. 51(5). 3265–3274. 3 indexed citations
3.
Reynolds, Tess, et al.. (2024). Universal non-circular cone beam CT orbits for metal artifact reduction imaging during image-guided procedures. Scientific Reports. 14(1). 26274–26274. 1 indexed citations
4.
Halliburton, Sandra S., Kai Mei, Amy E. Perkins, et al.. (2024). Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm. Physics in Medicine and Biology. 69(11). 115009–115009. 4 indexed citations
5.
6.
Liu, Anqi, Grace J. Gang, & J. Webster Stayman. (2024). Fourier diffusion for sparse CT reconstruction. PubMed. 11312. 37–37. 1 indexed citations
7.
Shapira, Nadav, Kai Mei, Michael Geagan, et al.. (2023). Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study. PNAS Nexus. 2(3). pgad026–pgad026. 7 indexed citations
8.
Reynolds, Tess, et al.. (2023). Technical note: Extended longitudinal and lateral 3D imaging with a continuous dual‐isocenter CBCT scan. Medical Physics. 50(4). 2372–2379. 3 indexed citations
9.
Shapira, Nadav, Pooyan Sahbaee, Grace J. Gang, et al.. (2023). Consistency of spectral results in cardiac dual-source photon-counting CT. Scientific Reports. 13(1). 8 indexed citations
10.
Boedeker, Kirsten, et al.. (2023). Notice of Removal: Fourier Diffusion Models: A Method to Control MTF and NPS in Score-Based Stochastic Image Generation. IEEE Transactions on Medical Imaging. PP. 1–1. 5 indexed citations
11.
Wang, Wenying, et al.. (2022). Design Optimization of Spatial-Spectral Filters for Cone-Beam CT Material Decomposition. IEEE Transactions on Medical Imaging. 41(9). 2399–2413. 5 indexed citations
12.
Gang, Grace J. & J. Webster Stayman. (2022). Universal orbit design for metal artifact elimination. Physics in Medicine and Biology. 67(11). 115008–115008. 8 indexed citations
13.
Gang, Grace J., Tina Ehtiati, Tess Reynolds, et al.. (2022). Non-circular CBCT orbit design and realization on a clinical robotic C-arm for metal artifact reduction. PubMed. 12034. 8–8. 6 indexed citations
14.
Li, Junyuan, et al.. (2022). Performance assessment framework for neural network denoising. PubMed. 10948. 64–64. 4 indexed citations
15.
Wang, Wenying, Grace J. Gang, Jeffrey H. Siewerdsen, & J. Webster Stayman. (2019). Volume-of-interest imaging using multiple aperture devices. PubMed. 5745. 74–74. 3 indexed citations
16.
Gang, Grace J., et al.. (2019). Dynamic fluence field modulation in computed tomography using multiple aperture devices. Physics in Medicine and Biology. 64(10). 105024–105024. 10 indexed citations
17.
Zhang, Hao, Grace J. Gang, Hao Dang, & J. Webster Stayman. (2018). Regularization Analysis and Design for Prior-Image-Based X-Ray CT Reconstruction. IEEE Transactions on Medical Imaging. 37(12). 2675–2686. 16 indexed citations
18.
Gang, Grace J., Jeffrey H. Siewerdsen, & J. Webster Stayman. (2017). Task-Driven Optimization of Fluence Field and Regularization for Model-Based Iterative Reconstruction in Computed Tomography. IEEE Transactions on Medical Imaging. 36(12). 2424–2435. 13 indexed citations
19.
Gang, Grace J., et al.. (2016). Design of dual multiple aperture devices for dynamical fluence field modulated CT.. PubMed. 2016. 29–32. 9 indexed citations
20.
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

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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