Pardis Ghafarian

944 total citations
72 papers, 665 citations indexed

About

Pardis Ghafarian is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Radiation. According to data from OpenAlex, Pardis Ghafarian has authored 72 papers receiving a total of 665 indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Radiology, Nuclear Medicine and Imaging, 44 papers in Biomedical Engineering and 23 papers in Radiation. Recurrent topics in Pardis Ghafarian's work include Medical Imaging Techniques and Applications (63 papers), Advanced X-ray and CT Imaging (43 papers) and Radiomics and Machine Learning in Medical Imaging (27 papers). Pardis Ghafarian is often cited by papers focused on Medical Imaging Techniques and Applications (63 papers), Advanced X-ray and CT Imaging (43 papers) and Radiomics and Machine Learning in Medical Imaging (27 papers). Pardis Ghafarian collaborates with scholars based in Iran, United States and Switzerland. Pardis Ghafarian's co-authors include Mohammad Reza Ay, Arman Rahmim, Parham Geramifar, Habib Zaidi, Isaac Shiri, Ghasem Hajianfar, Saeed Sarkar, Hossein Ghadiri, Navid Zeraatkar and Kevin Leung and has published in prestigious journals such as SHILAP Revista de lepidopterología, Physics in Medicine and Biology and Medical Physics.

In The Last Decade

Pardis Ghafarian

67 papers receiving 655 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pardis Ghafarian Iran 14 586 308 192 97 25 72 665
Joscha Maier Germany 16 623 1.1× 557 1.8× 147 0.8× 101 1.0× 9 0.4× 61 730
Alireza Kamali‐Asl Iran 11 322 0.5× 159 0.5× 123 0.6× 82 0.8× 12 0.5× 66 437
Kirsten Boedeker United States 10 804 1.4× 730 2.4× 122 0.6× 291 3.0× 11 0.4× 28 1.0k
Markus Oelhafen Switzerland 11 390 0.7× 222 0.7× 221 1.2× 61 0.6× 8 0.3× 20 420
Azadeh Akhavanallaf Switzerland 13 528 0.9× 252 0.8× 143 0.7× 108 1.1× 61 2.4× 29 626
Tong Xu United States 12 316 0.5× 243 0.8× 160 0.8× 175 1.8× 14 0.6× 42 436
Tao Sun China 14 408 0.7× 186 0.6× 153 0.8× 55 0.6× 7 0.3× 69 522
Joshua Schaefferkoetter United States 11 351 0.6× 128 0.4× 92 0.5× 57 0.6× 18 0.7× 22 434
Hadi Fayad France 13 476 0.8× 132 0.4× 266 1.4× 167 1.7× 10 0.4× 45 609
Piotr Maniawski United States 6 570 1.0× 169 0.5× 154 0.8× 66 0.7× 5 0.2× 18 609

Countries citing papers authored by Pardis Ghafarian

Since Specialization
Citations

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

Fields of papers citing papers by Pardis Ghafarian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pardis Ghafarian

This figure shows the co-authorship network connecting the top 25 collaborators of Pardis Ghafarian. A scholar is included among the top collaborators of Pardis Ghafarian 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 Pardis Ghafarian. Pardis Ghafarian 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.
Sheikhzadeh, Peyman, et al.. (2024). Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study. Biomedical Physics & Engineering Express. 10(4). 45019–45019.
2.
Rezaei, Hadi, Peyman Sheikhzadeh, Pardis Ghafarian, Habib Zaidi, & Mohammad Reza Ay. (2023). Accurate modeling and performance evaluation of a total‐body pet scanner using Monte Carlo simulations. Medical Physics. 50(11). 6815–6827. 7 indexed citations
3.
Kamali‐Asl, Alireza, et al.. (2023). List-mode quantitative joint reconstruction of activity and attenuation maps in Time-of-Flight PET. Journal of Instrumentation. 18(9). P09041–P09041. 2 indexed citations
4.
5.
Sarkar, Saeed, et al.. (2021). Leveraging deep neural networks to improve numerical and perceptual image quality in low-dose preclinical PET imaging. Computerized Medical Imaging and Graphics. 94. 102010–102010. 5 indexed citations
6.
Ghafarian, Pardis, et al.. (2020). Validation and evaluation of a GATE model for MAMMI PET scanner. 28(1). 33–38.
7.
Ghafarian, Pardis, et al.. (2019). The influence of misregistration between CT and SPECT images on the accuracy of CT-based attenuation correction of cardiac SPECT/CT imaging: Phantom and clinical studies. 27(2). 63–72. 8 indexed citations
8.
Salimi, Yazdan, Mohammad Reza Deevband, Pardis Ghafarian, & Mohammad Reza Ay. (2018). Uncertainties in effective dose estimation for CT transmission scan in total body PET-CT imaging with Auto mA3D tube current modulation. Iranian Journal of radiation research. 16(4). 465–472. 5 indexed citations
9.
Ghafarian, Pardis, et al.. (2018). The influence of using different reconstruction algorithms on sensitivity of quantitative 18F-FDG-PET volumetric measures to background activity variation. 26(2). 87–97. 3 indexed citations
10.
11.
Sheikhzadeh, Peyman, Hamid Sabet, Hossein Ghadiri, et al.. (2017). Development and validation of an accurate GATE model for NeuroPET scanner. Physica Medica. 40. 59–65. 6 indexed citations
12.
Zeraatkar, Navid, et al.. (2016). Design and development of a dedicated portable gamma camera system for intra-operative imaging. Physica Medica. 32(7). 889–897. 9 indexed citations
13.
Shekari, Mahnaz, et al.. (2015). Optimizing Image Reconstruction Parameters in Time of Flight PET/CT Imaging: a Phantom Study. SHILAP Revista de lepidopterología. 2(3). 146–154. 3 indexed citations
14.
Ghafarian, Pardis, et al.. (2015). The Impact of Point Spread Function Modeling on Scan Duration in PET Imaging. SHILAP Revista de lepidopterología. 2(3). 137–145. 1 indexed citations
15.
Ghafarian, Pardis & Mohammad Reza Ay. (2014). The Influence of PET and CT Misalignment due to Respiratory Motion on the Cardiac PET/CT Imaging: a Simulation Study. SHILAP Revista de lepidopterología. 1 indexed citations
16.
Ghafarian, Pardis, Mohammad Reza Ay, Armaghan Fard‐Esfahani, Arman Rahmim, & Habib Zaidi. (2014). Quantification of PET and CT Misalignment Errors Due to Bulk Motion in Cardiac PET/CT Imaging: Phantom and Clinical Studies. SHILAP Revista de lepidopterología. 1(3). 159–167. 1 indexed citations
17.
Ghadiri, Hossein, et al.. (2014). Performance Evaluation of Bone Mineral Densitometry Techniques by a Novel Phantom. SHILAP Revista de lepidopterología. 1(4). 271–278. 2 indexed citations
18.
Ghafarian, Pardis, et al.. (2014). A hybrid method for generation of attenuation map for MR-based attenuation correction of PET data in prostate PET/MR imaging. EJNMMI Physics. 1(S1). A77–A77. 1 indexed citations
19.
20.
Ay, Mustafa, et al.. (2004). Measurement of organ dose in abdomen-pelvis CT exam as a function of mA, KV and scanner type by Monte Carlo method. Iranian Journal of radiation research. 1(4). 187–194. 6 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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