Paul Segars

686 total citations · 1 hit paper
45 papers, 496 citations indexed

About

Paul Segars is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Radiation. According to data from OpenAlex, Paul Segars has authored 45 papers receiving a total of 496 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Radiology, Nuclear Medicine and Imaging, 16 papers in Biomedical Engineering and 12 papers in Radiation. Recurrent topics in Paul Segars's work include Medical Imaging Techniques and Applications (29 papers), Advanced X-ray and CT Imaging (14 papers) and Radiation Dose and Imaging (12 papers). Paul Segars is often cited by papers focused on Medical Imaging Techniques and Applications (29 papers), Advanced X-ray and CT Imaging (14 papers) and Radiation Dose and Imaging (12 papers). Paul Segars collaborates with scholars based in United States, Canada and Switzerland. Paul Segars's co-authors include Tina Morrison, Dinesh Manocha, Ming C. Lin, Russell Gayle, Mathias Peirlinck, Julius M. Guccione, Ellen Kuhl, Francisco Sahli Costabal, Ehsan Samei and Derya Öztürk and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, Physics in Medicine and Biology and Medical Physics.

In The Last Decade

Paul Segars

39 papers receiving 486 citations

Hit Papers

Precision medicine in human heart modeling 2021 2026 2022 2024 2021 40 80 120
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. Precision medicine in human heart modeling
    2021 134 citations Mathias Peirlinck, Francisco Sahli Costabal et al. Biomechanics and Modeling in Mechanobiology profile →

Peers

Paul Segars
Ming Yuchi China
Dirk Schäfer Germany
Junyi Xia United States
Elena Faggiano Italy
Saša Grbić United States
Alexander Brost Germany
Jiwoong Jeong United States
Mehdi H. Moghari United States
Paul M. Novotny United States
Ming Yuchi China
Paul Segars
Citations per year, relative to Paul Segars Paul Segars (= 1×) peers Ming Yuchi

Countries citing papers authored by Paul Segars

Since Specialization
Citations

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

Fields of papers citing papers by Paul Segars

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Segars

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Segars. A scholar is included among the top collaborators of Paul Segars 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 Paul Segars. Paul Segars 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.
Salinas, Christine M., et al.. (2025). In-silico CT simulations of deep learning generated heterogeneous phantoms. Biomedical Physics & Engineering Express. 11(4). 45027–45027. 1 indexed citations
2.
3.
Abadi, Ehsan, et al.. (2025). The potential of photon-counting CT for the improved precision of lung nodule radiomics. Physics in Medicine and Biology. 70(3). 35001–35001.
4.
Schwartz, Fides R., Faraz Farhadi, Mojtaba Zarei, et al.. (2024). Technology Characterization Through Diverse Evaluation Methodologies: Application to Thoracic Imaging in Photon-Counting Computed Tomography. Journal of Computer Assisted Tomography. 49(1). 113–124. 2 indexed citations
5.
Li, Xuelong, et al.. (2024). A Reinforcement Learning Approach to Automate Breast Radiation Therapy Treatment Planning Using Electronic Compensation (ECOMP). International Journal of Radiation Oncology*Biology*Physics. 120(2). S63–S63.
6.
Herts, Brian R., et al.. (2023). Effect of tin spectral filtration on organ and effective dose in CT colonography and CT lung cancer screening. Medical Physics. 51(1). 103–112. 3 indexed citations
7.
Segars, Paul, et al.. (2023). Unsupervised synthesis of realistic coronary artery X-ray angiogram. International Journal of Computer Assisted Radiology and Surgery. 18(12). 2329–2338.
8.
Sharma, Shobhit, Debashish Pal, Ehsan Abadi, et al.. (2023). Deep silicon photon-counting CT: A first simulation-based study for assessing perceptual benefits across diverse anatomies. European Journal of Radiology. 171. 111279–111279. 2 indexed citations
9.
Subashi, Ergys, Li Feng, Yilin Liu, et al.. (2023). View-sharing for 4D magnetic resonance imaging with randomized projection-encoding enables improvements of respiratory motion imaging for treatment planning in abdominothoracic radiotherapy. Physics and Imaging in Radiation Oncology. 25. 100409–100409. 2 indexed citations
10.
Subashi, Ergys, et al.. (2023). A model for gastrointestinal tract motility in a 4D imaging phantom of human anatomy. Medical Physics. 50(5). 3066–3075. 5 indexed citations
11.
12.
Miró, Joaquim, et al.. (2022). Patient-specific Cardio-respiratory Motion Prediction in X-ray Angiography using LSTM Networks. Physics in Medicine and Biology. 68(2). 25010–25010. 2 indexed citations
13.
Peirlinck, Mathias, Francisco Sahli Costabal, Yao Jiang, et al.. (2021). Precision medicine in human heart modeling. Biomechanics and Modeling in Mechanobiology. 20(3). 803–831. 134 indexed citations breakdown →
14.
Chen, Zi‐Jiang, Andrzej Przekwas, Paul Segars, et al.. (2020). Anthropometry‐based generation of personalized and population‐specific human airway models. International Journal for Numerical Methods in Biomedical Engineering. 36(5). e3324–e3324. 4 indexed citations
15.
Abadi, Ehsan, et al.. (2016). TH-AB-207A-01: Contrast-Enhanced CT: Correlation of Radiation Dose and Biological Effect. Medical Physics. 43(6Part44). 3859–3859. 1 indexed citations
16.
Sahbaee, Pooyan, Yuan Li, Paul Segars, et al.. (2015). Determination of contrast media administration to achieve a targeted contrast enhancement in CT. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9412. 941205–941205. 1 indexed citations
17.
Hu, Nan, Laura Cerviño, Paul Segars, et al.. (2014). A method for generating large datasets of organ geometries for radiotherapy treatment planning studies. Radiology and Oncology. 48(4). 408–415. 1 indexed citations
18.
Könik, Arda, Karen L. Johnson, Paul Segars, et al.. (2014). Digital anthropomorphic phantoms of non-rigid human respiratory and voluntary body motion for investigating motion correction in emission imaging. Physics in Medicine and Biology. 59(14). 3669–3682. 14 indexed citations
19.
Hobbs, Robert F., et al.. (2013). Improved dose–volume histogram estimates for radiopharmaceutical therapy by optimizing quantitative SPECT reconstruction parameters. Physics in Medicine and Biology. 58(11). 3631–3647. 22 indexed citations
20.
Minarik, David, Michael Ljungberg, Paul Segars, & Katarina Sjögreen Gleisner. (2009). Evaluation of quantitative planar90Y bremsstrahlung whole-body imaging. Physics in Medicine and Biology. 54(19). 5873–5883. 34 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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