Eugene Ozhinsky

1.1k total citations
47 papers, 700 citations indexed

About

Eugene Ozhinsky is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Rheumatology. According to data from OpenAlex, Eugene Ozhinsky has authored 47 papers receiving a total of 700 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Radiology, Nuclear Medicine and Imaging, 28 papers in Biomedical Engineering and 11 papers in Rheumatology. Recurrent topics in Eugene Ozhinsky's work include Ultrasound and Hyperthermia Applications (19 papers), Advanced MRI Techniques and Applications (17 papers) and Ultrasound Imaging and Elastography (10 papers). Eugene Ozhinsky is often cited by papers focused on Ultrasound and Hyperthermia Applications (19 papers), Advanced MRI Techniques and Applications (17 papers) and Ultrasound Imaging and Elastography (10 papers). Eugene Ozhinsky collaborates with scholars based in United States, Germany and Netherlands. Eugene Ozhinsky's co-authors include Sharmila Majumdar, Michael D. Ries, Viola Rieke, Ying Lü, Vikas V. Patel, Katherine S. Hall, Chris J. Diederich, Sarah J. Nelson, Valentina Pedoia and Thomas M. Link and has published in prestigious journals such as Nature Communications, Journal of Bone and Joint Surgery and Radiology.

In The Last Decade

Eugene Ozhinsky

47 papers receiving 686 citations

Peers

Eugene Ozhinsky

RNMI

SURGE

RHEUM

OSM

Benjamin Fritz Switzerland

Xeni Deligianni Switzerland

Saeed Jerban United States

Antonio J. Machado United States

Iman Khodarahmi United States

Esther Raithel Germany

Johannes Peeters Netherlands

Azadeh Sharafi United States

Vladimír Juráš Austria

Emily J. McWalter United States

Benjamin Fritz Switzerland

Eugene Ozhinsky

456 ×1.3

478 ×1.6

RNMI

617 ×2.9

SURGE

197 ×1.4

RHEUM

252 ×2.6

OSM

Citations per year, relative to Eugene Ozhinsky Eugene Ozhinsky (= 1×) peers Benjamin Fritz

Countries citing papers authored by Eugene Ozhinsky

Since Specialization

Citations

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

Fields of papers citing papers by Eugene Ozhinsky

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eugene Ozhinsky

This figure shows the co-authorship network connecting the top 25 collaborators of Eugene Ozhinsky. A scholar is included among the top collaborators of Eugene Ozhinsky 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 Eugene Ozhinsky. Eugene Ozhinsky is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Zhu, Yang, Chong Lai, Zuwei Ma, et al.. (2024). Shape-recovery of implanted shape-memory devices remotely triggered via image-guided ultrasound heating. Nature Communications. 15(1). 1123–1123. 21 indexed citations

Ozhinsky, Eugene, Felix Liu, Valentina Pedoia, & Sharmila Majumdar. (2024). Machine learning-based automated scan prescription of lumbar spine MRI acquisitions. Magnetic Resonance Imaging. 110. 29–34. 2 indexed citations

Kim, Ki Soo, Kazim Narsinh, & Eugene Ozhinsky. (2024). Technical advances in motion‐robust MR thermometry. Magnetic Resonance in Medicine. 92(1). 15–27. 5 indexed citations

Kim, Ki Soo, Yi Li, Orit A. Glenn, et al.. (2023). Estimate of fetal brain temperature using proton resonance frequency thermometry during 3 Tesla fetal magnetic resonance imaging. Quantitative Imaging in Medicine and Surgery. 13(12). 7987–7995. 1 indexed citations

Kim, Ki Soo, Lubdha M. Shah, Bhavya Shah, et al.. (2023). MR Thermometry during Transcranial MR Imaging–Guided Focused Ultrasound Procedures: A Review. American Journal of Neuroradiology. 45(1). 1–8. 14 indexed citations

Calivá, Francesco, Nikan K. Namiri, Maureen Dubreuil, et al.. (2021). Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging. Nature Reviews Rheumatology. 18(2). 112–121. 24 indexed citations

Schacky, Claudio E. von, Jae Ho Sohn, Felix Liu, et al.. (2020). Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs. Radiology. 295(1). 136–145. 75 indexed citations

Schacky, Claudio E. von, Jae Ho Sohn, Sarah C. Foreman, et al.. (2020). Development and performance comparison with radiologists of a multitask deep learning model for severity grading of hip osteoarthritis features on radiographs. Osteoarthritis and Cartilage. 28. S306–S308. 4 indexed citations

Ozhinsky, Eugene, Rutwik Shah, Kay M. Crossley, et al.. (2020). Computer‐Aided Detection AI Reduces Interreader Variability in Grading Hip Abnormalities With MRI. Journal of Magnetic Resonance Imaging. 52(4). 1163–1172. 19 indexed citations

10.

Ozhinsky, Eugene, et al.. (2019). Detecting hip osteoarthritic degenerative changes in MRI using deep learning. Osteoarthritis and Cartilage. 27. S387–S388. 3 indexed citations

11.

Bucknor, Matthew D., et al.. (2019). The impact of technical parameters on ablation volume during MR-guided focused ultrasound of desmoid tumors. International Journal of Hyperthermia. 36(1). 472–475. 3 indexed citations

12.

Ozhinsky, Eugene, Vasant A. Salgaonkar, Chris J. Diederich, & Viola Rieke. (2018). MR thermometry-guided ultrasound hyperthermia of user-defined regions using the ExAblate prostate ablation array. Journal of Therapeutic Ultrasound. 6(1). 7–7. 18 indexed citations

13.

Han, Misung, Eugene Ozhinsky, Peder E. Z. Larson, et al.. (2017). Assessing temperature changes in cortical bone using variable flip-angle ultrashort echo-time MRI. AIP conference proceedings. 1821. 60001–60001. 3 indexed citations

14.

Salgaonkar, Vasant A., Punit Prakash, Viola Rieke, et al.. (2014). Model‐based feasibility assessment and evaluation of prostate hyperthermia with a commercial MR‐guided endorectal HIFU ablation array. Medical Physics. 41(3). 33301–33301. 20 indexed citations

15.

Ozhinsky, Eugene. (2012). Automated Acquisition of Brain MRSI Data. eScholarship (California Digital Library). 1 indexed citations

16.

Carballido‐Gamio, Julio, et al.. (2008). MRI cartilage of the knee: segmentation, analysis, and visualization.. 3 indexed citations

17.

Bauer, Jan S., Stefanie Krause, Colin J.D. Ross, et al.. (2006). Volumetric Cartilage Measurements of Porcine Knee at 1.5-T and 3.0-T MR Imaging: Evaluation of Precision and Accuracy. Radiology. 241(2). 399–406. 28 indexed citations

18.

Dunn, Timothy C., et al.. (2004). Computer-aided quantification of focal cartilage lesions of osteoarthritic knee using MRI. Magnetic Resonance Imaging. 22(8). 1105–1115. 12 indexed citations

19.

Pothuaud, Laurent, et al.. (2004). A New Computational Efficient Approach for Trabecular Bone Analysis using Beam Models Generated with Skeletonized Graph Technique. Computer Methods in Biomechanics & Biomedical Engineering. 7(4). 205–213. 29 indexed citations

20.

Patel, Vikas V., Katherine S. Hall, Michael D. Ries, et al.. (2003). MAGNETIC RESONANCE IMAGING OF PATELLOFEMORAL KINEMATICS WITH WEIGHT-BEARING. Journal of Bone and Joint Surgery. 85(12). 2419–2424. 79 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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