Parham Pezeshk

665 total citations
45 papers, 397 citations indexed

About

Parham Pezeshk is a scholar working on Surgery, Rheumatology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Parham Pezeshk has authored 45 papers receiving a total of 397 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Surgery, 13 papers in Rheumatology and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Parham Pezeshk's work include Musculoskeletal synovial abnormalities and treatments (7 papers), Orthopedic Infections and Treatments (5 papers) and Bone and Joint Diseases (5 papers). Parham Pezeshk is often cited by papers focused on Musculoskeletal synovial abnormalities and treatments (7 papers), Orthopedic Infections and Treatments (5 papers) and Bone and Joint Diseases (5 papers). Parham Pezeshk collaborates with scholars based in United States, United Kingdom and Iran. Parham Pezeshk's co-authors include Avneesh Chhabra, Yin Xi, Mohammadali M. Shoja, Vibhor Wadhwa, R. Shane Tubbs, Yogesh Kumar, Lauren Phillips, Fereidoun Azizi, Lucinda Cacicedo and Ali A. Ghazi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of Magnetic Resonance Imaging and European Radiology.

In The Last Decade

Parham Pezeshk

38 papers receiving 388 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Parham Pezeshk United States 12 135 116 79 76 76 45 397
Gregory J. Czuczman United States 10 238 1.8× 45 0.4× 44 0.6× 71 0.9× 23 0.3× 18 356
Madeleine Sampson United Kingdom 12 351 2.6× 70 0.6× 47 0.6× 61 0.8× 62 0.8× 34 604
Anthony G. Ryan Ireland 13 246 1.8× 49 0.4× 112 1.4× 111 1.5× 31 0.4× 29 407
Ian Blair Fries United States 11 305 2.3× 75 0.6× 57 0.7× 48 0.6× 127 1.7× 11 504
Nigel Rozario United States 13 189 1.4× 53 0.5× 61 0.8× 30 0.4× 23 0.3× 39 513
Richard G. Kavanagh Ireland 15 304 2.3× 143 1.2× 121 1.5× 26 0.3× 31 0.4× 32 625
Maria Pilar Aparisi Gómez New Zealand 11 104 0.8× 50 0.4× 33 0.4× 56 0.7× 148 1.9× 51 465
Z Khan United States 10 96 0.7× 56 0.5× 43 0.5× 32 0.4× 15 0.2× 36 322
Duygu Herek Türkiye 11 166 1.2× 93 0.8× 80 1.0× 28 0.4× 35 0.5× 44 360
Wagner Iared Brazil 13 152 1.1× 127 1.1× 128 1.6× 19 0.3× 24 0.3× 34 498

Countries citing papers authored by Parham Pezeshk

Since Specialization
Citations

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

Fields of papers citing papers by Parham Pezeshk

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Parham Pezeshk

This figure shows the co-authorship network connecting the top 25 collaborators of Parham Pezeshk. A scholar is included among the top collaborators of Parham Pezeshk 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 Parham Pezeshk. Parham Pezeshk 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.
Ashikyan, Oganes, Parham Pezeshk, Yin Xi, et al.. (2025). Are artificial intelligence generated lower extremity radiographic measurements accurate in a cohort with implants?. Skeletal Radiology. 54(10). 2097–2106.
2.
3.
Ashikyan, Oganes, Parham Pezeshk, Yin Xi, et al.. (2024). Reliability assessment of leg length and angular alignment on manual reads versus artificial intelligence-generated lower extremity radiographic measurements. Clinical Imaging. 113. 110233–110233. 1 indexed citations
4.
Pezeshk, Parham, et al.. (2023). Spectrum of Hand Arthritis. Magnetic Resonance Imaging Clinics of North America. 31(2). 239–253. 1 indexed citations
5.
Pezeshk, Parham, et al.. (2023). Diffusion weighted imaging of extremity bone tumors—inter-reader analysis and incremental value over conventional MR imaging. British Journal of Radiology. 96(1151). 20230352–20230352. 1 indexed citations
6.
Ashikyan, Oganes, Parham Pezeshk, Yin Xi, et al.. (2023). Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination. Skeletal Radiology. 53(5). 923–933. 11 indexed citations
7.
Pezeshk, Parham, et al.. (2023). Extent of bone marrow edema on dual-energy CT aids in differentiation of acute from post-acute fractures of lower legs. European Radiology. 33(6). 4094–4102. 1 indexed citations
8.
Chen, Liyuan, Jing Wang, Michael R. Folkert, et al.. (2021). Convolutional Neural Networks Accurately Predict Benign versus Malignant Status Among Peripheral Nerve Sheath Tumors. 6(1). 1 indexed citations
9.
Zuniga, John R., et al.. (2021). Does Presurgical Magnetic Resonance Neurography Predict Surgical Gap Size in Trigeminal Class IV and V Injuries?. Journal of Oral and Maxillofacial Surgery. 79(12). 2574–2581. 2 indexed citations
10.
Pezeshk, Parham, et al.. (2021). 3-T MRI of the Ankle Tendons and Ligaments. Clinics in Sports Medicine. 40(4). 731–754. 3 indexed citations
11.
12.
Ashikyan, Oganes, et al.. (2019). Evaluation of giant cell tumors by diffusion weighted imaging–fractional ADC analysis. Skeletal Radiology. 48(11). 1765–1773. 14 indexed citations
13.
14.
Wang, Xinzeng, et al.. (2018). Frequency Offset Corrected Inversion Pulse for B0 and B1 Insensitive Fat Suppression at 3T: Application to MR Neurography of Brachial Plexus. Journal of Magnetic Resonance Imaging. 48(4). 1104–1111. 9 indexed citations
15.
McKinney, Alexander M., Rakhee Gawande, Parham Pezeshk, Charles L. Truwit, & Jeffrey Rykken. (2015). Preliminary experience with intravenous gadoxetate disodium as a craniospinal MR contrast agent. European Journal of Radiology. 84(12). 2539–2547. 3 indexed citations
16.
Pezeshk, Parham, et al.. (2006). Usefulness of 18F-FDG PET–Directed Skeletal Biopsy for Metastatic Neoplasm. Academic Radiology. 13(8). 1011–1015. 8 indexed citations
17.
Etemadi, Arash, et al.. (2004). Metabolic syndrome and development of diabetes and impaired glucose tolerance. Majallah-i ghudad-i darūn/rīz va mitābulīsm-i Īrān./Majallah-i ghudad-i darūn/rīz va mitābulīsm-i Īrān.. 6(1). 47–54. 1 indexed citations
18.
Ghazi, Ali A., et al.. (2004). Seasonal variation of serum 25 hydroxy D3 in residents of Tehran. Journal of Endocrinological Investigation. 27(7). 676–679. 36 indexed citations
19.
Pezeshk, Parham, et al.. (2003). Impact of hemodialysis on serum paraoxonase activity and total antioxidant capacity in patients with end –stage renal disease. Research in Medicine. 27(4). 265–272.
20.
Etemadi, Arash, et al.. (2003). LIPIDS, APOLIPOPROTEINS, LIPID OXIDATION AND PARAOXONASE ENZYME ACTIVITY IN DIABETIC AND NON-DIABETIC END STAGE RENAL DISEASE PATIENTS. Majallah-i ghudad-i darūn/rīz va mitābulīsm-i Īrān./Majallah-i ghudad-i darūn/rīz va mitābulīsm-i Īrān.. 5(117). 27–32. 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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