Sarv Priya

974 total citations
76 papers, 599 citations indexed

About

Sarv Priya is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Sarv Priya has authored 76 papers receiving a total of 599 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Radiology, Nuclear Medicine and Imaging, 20 papers in Pulmonary and Respiratory Medicine and 19 papers in Biomedical Engineering. Recurrent topics in Sarv Priya's work include Radiomics and Machine Learning in Medical Imaging (17 papers), Advanced X-ray and CT Imaging (11 papers) and Glioma Diagnosis and Treatment (9 papers). Sarv Priya is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (17 papers), Advanced X-ray and CT Imaging (11 papers) and Glioma Diagnosis and Treatment (9 papers). Sarv Priya collaborates with scholars based in United States, India and United Kingdom. Sarv Priya's co-authors include Girish Bathla, Neetu Soni, Prashant Nagpal, Caitlin Ward, Michael L. Steigner, Arun Sharma, Varun Monga, Richard Thomas, Milan Sonka and Thomas Locke and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and The American Journal of Medicine.

In The Last Decade

Sarv Priya

65 papers receiving 587 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sarv Priya United States 15 308 186 186 141 103 76 599
Terje Nome Norway 13 247 0.8× 168 0.9× 88 0.5× 130 0.9× 109 1.1× 23 665
Johann-Martin Hempel Germany 17 424 1.4× 242 1.3× 173 0.9× 192 1.4× 53 0.5× 51 768
Jianping Dai China 17 408 1.3× 262 1.4× 231 1.2× 89 0.6× 36 0.3× 41 783
Hwiyoung Kim South Korea 18 466 1.5× 195 1.0× 143 0.8× 40 0.3× 215 2.1× 42 902
Lenhard Pennig Germany 17 425 1.4× 75 0.4× 288 1.5× 134 1.0× 225 2.2× 91 841
Karoline Skogen Norway 9 488 1.6× 152 0.8× 149 0.8× 60 0.4× 85 0.8× 23 675
Jay Patel United States 15 447 1.5× 205 1.1× 179 1.0× 25 0.2× 115 1.1× 31 725
Beomseok Sohn South Korea 11 257 0.8× 44 0.2× 136 0.7× 62 0.4× 79 0.8× 39 499
Abdelkader Mahammedi United States 11 137 0.4× 76 0.4× 156 0.8× 112 0.8× 43 0.4× 26 568
Yangsean Choi South Korea 17 369 1.2× 75 0.4× 92 0.5× 55 0.4× 78 0.8× 54 729

Countries citing papers authored by Sarv Priya

Since Specialization
Citations

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

Fields of papers citing papers by Sarv Priya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sarv Priya

This figure shows the co-authorship network connecting the top 25 collaborators of Sarv Priya. A scholar is included among the top collaborators of Sarv Priya 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 Sarv Priya. Sarv Priya 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.
2.
Priya, Sarv, et al.. (2023). Optimizing Deep Learning for Cardiac MRI Segmentation: The Impact of Automated Slice Range Classification. Academic Radiology. 31(2). 503–513. 3 indexed citations
3.
4.
Bathla, Girish, Neetu Soni, Nicholas B. Larson, et al.. (2023). AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods. Journal of Neuroradiology. 51(3). 258–264. 19 indexed citations
5.
Priya, Sarv, et al.. (2023). ComBat Harmonization of Myocardial Radiomic Features Sensitive to Cardiac MRI Acquisition Parameters. Radiology Cardiothoracic Imaging. 5(4). e220312–e220312. 7 indexed citations
6.
Zou, Qing, et al.. (2022). Variational Manifold Learning From Incomplete Data: Application to Multislice Dynamic MRI. IEEE Transactions on Medical Imaging. 41(12). 3552–3561. 7 indexed citations
7.
Bathla, Girish, et al.. (2022). Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke. Journal of Stroke and Cerebrovascular Diseases. 31(11). 106757–106757. 3 indexed citations
8.
Bathla, Girish, Neetu Soni, M. Hayakawa, et al.. (2022). CT Perfusion Maps Improve Detection of M2-MCA Occlusions in Acute Ischemic Stroke. Journal of Stroke and Cerebrovascular Diseases. 31(6). 106473–106473. 19 indexed citations
9.
Priya, Sarv, Caitlin Ward, Thomas Locke, et al.. (2021). Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study. The Neuroradiology Journal. 34(4). 320–328. 14 indexed citations
10.
Nagpal, Prashant, et al.. (2021). Left ventricular assist device pseudo-thrombosis due to use of metal artifact reduction algorithm on cardiac CT. Journal of cardiovascular computed tomography. 16(1). e1–e2. 1 indexed citations
11.
12.
Priya, Sarv, Yanan Liu, Caitlin Ward, et al.. (2021). Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?. Cancers. 13(11). 2568–2568. 19 indexed citations
13.
Priya, Sarv, Tanya Aggarwal, Caitlin Ward, et al.. (2021). Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison—Cardiac MRI Radiomics in Pulmonary Hypertension. Journal of Clinical Medicine. 10(9). 1921–1921. 9 indexed citations
14.
Bathla, Girish, Amit Agarwal, Tracey Cho, et al.. (2021). Vascular Involvement in Neurosarcoidosis. Neurology Neuroimmunology & Neuroinflammation. 8(6). 20 indexed citations
15.
Soni, Neetu, et al.. (2020). Bartonella osteomyelitis versus vertebral sarcoidosis: A tale of two cases. The Neuroradiology Journal. 34(2). 140–146. 1 indexed citations
16.
Kandemirli, Sedat Giray, Saurav Chopra, Sarv Priya, et al.. (2020). Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging. Clinical Neurology and Neurosurgery. 198. 106205–106205. 27 indexed citations
17.
Bathla, Girish, Sarv Priya, Edgar A. Samaniego, et al.. (2020). Cerebral computed tomographic angiography using third-generation reconstruction algorithm provides improved image quality with lower contrast and radiation dose. Neuroradiology. 62(8). 965–970. 3 indexed citations
18.
Priya, Sarv, et al.. (2020). Pulmonary embolism rule out: positivity and factors affecting the yield of CT angiography. Postgraduate Medical Journal. 96(1140). 594–599. 9 indexed citations
19.
Soni, Neetu, Sarv Priya, & Girish Bathla. (2019). Texture Analysis in Cerebral Gliomas: A Review of the Literature. American Journal of Neuroradiology. 40(6). 928–934. 79 indexed citations
20.
Priya, Sarv, et al.. (2017). A REVIEW ON THE ROLE OF AVARANA (OCCLUSION OF BODY CHANNELS) IN METABOLIC SYNDROME. International Journal of Ayurveda and Pharma Research. 5(6). 1 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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