Tejas Parekh

413 total citations
9 papers, 92 citations indexed

About

Tejas Parekh is a scholar working on Biomedical Engineering, Radiology, Nuclear Medicine and Imaging and Aerospace Engineering. According to data from OpenAlex, Tejas Parekh has authored 9 papers receiving a total of 92 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Biomedical Engineering, 4 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Aerospace Engineering. Recurrent topics in Tejas Parekh's work include Superconducting Materials and Applications (4 papers), Cardiac Imaging and Diagnostics (3 papers) and Particle accelerators and beam dynamics (3 papers). Tejas Parekh is often cited by papers focused on Superconducting Materials and Applications (4 papers), Cardiac Imaging and Diagnostics (3 papers) and Particle accelerators and beam dynamics (3 papers). Tejas Parekh collaborates with scholars based in United States, India and United Kingdom. Tejas Parekh's co-authors include Piotr J. Slomka, Marcelo F. Di Carli, Daniel S. Berman, Serge D. Van Kriekinge, Paul Kavanagh, Joanna X. Liang, Robert J.H. Miller, Yuka Otaki, Ananya Singh and Damini Dey and has published in prestigious journals such as Journal of the American College of Cardiology, European Journal of Nuclear Medicine and Molecular Imaging and Computers in Biology and Medicine.

In The Last Decade

Tejas Parekh

9 papers receiving 92 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Tejas Parekh United States	5	51	34	16	15	13	9	92
Pinjing Cheng China	6	26 0.5×	5 0.1×	6 0.4×	13 0.9×	8 0.6×	13	63
D. Žontar Slovenia	9	69 1.4×	30 0.9×	77 4.8×	11 0.7×	2 0.2×	21	182
F. Albiol Spain	6	31 0.6×	18 0.5×	4 0.3×		4 0.3×	17	76
Tadao Kuwano Japan	7	53 1.0×	18 0.5×	15 0.9×		7 0.5×	22	109
C. Ray France	7	40 0.8×	9 0.3×	9 0.6×	2 0.1×		15	222
J. Weingarten Germany	6	23 0.5×	11 0.3×	57 3.6×		6 0.5×	24	112
Kinda Anna Saddi United Kingdom	5	76 1.5×	31 0.9×	1 0.1×	6 0.4×	4 0.3×	7	107
T. Vik Germany	6	39 0.8×	14 0.4×	13 0.8×	1 0.1×	5 0.4×	15	85
M. Wiesmann Germany	4	38 0.7×	52 1.5×	11 0.7×	1 0.1×	2 0.2×	6	101
J. Garay Garcia Spain	9	67 1.3×	86 2.5×	61 3.8×		3 0.2×	12	160

Countries citing papers authored by Tejas Parekh

Since Specialization

Citations

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

Fields of papers citing papers by Tejas Parekh

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tejas Parekh

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

All Works

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9 of 9 papers shown

Miller, Robert J.H., Tali Sharir, Andrew J. Einstein, et al.. (2022). Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry. Computers in Biology and Medicine. 145. 105449–105449. 21 indexed citations

Singh, Ananya, Jacek Kwieciński, Robert J.H. Miller, et al.. (2022). Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images. Circulation Cardiovascular Imaging. 15(9). e014526–e014526. 25 indexed citations

Singh, Ananya, Konrad Pieszko, Aakash Shanbhag, et al.. (2022). IMPROVED MORTALITY RISK ASSESSMENT FROM MYOCARDIAL PET FLOW, PERFUSION AND CALCIUM SCORES USING ARTIFICIAL INTELLIGENCE. Journal of the American College of Cardiology. 79(9). 1182–1182. 1 indexed citations

Otaki, Yuka, Serge D. Van Kriekinge, Paul Kavanagh, et al.. (2021). Improved myocardial blood flow estimation with residual activity correction and motion correction in 18F-flurpiridaz PET myocardial perfusion imaging. European Journal of Nuclear Medicine and Molecular Imaging. 49(6). 1881–1893. 21 indexed citations

Parekh, Tejas, et al.. (2021). A Multi-Stage Deep Transfer Learning Method for Classification of Diabetic Retinopathy in Retinal images. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC). abs 1811 1238. 1143–1149. 2 indexed citations

Patel, Hitesh, et al.. (2017). Commissioning and experimental validation of SST-1 plasma facing components. Journal of Physics Conference Series. 823. 12062–12062. 1 indexed citations

Gupta, Manoj Kumar, et al.. (2012). Integrated Leak testing of 80 K Thermal Shields of SST-1 in Room Temperature and Cold condition. 38. 127–131. 2 indexed citations

Gupta, N.C., et al.. (2012). Performance validation tests on 80K bubble type of shields for SST-1. Cryogenics. 52(12). 685–688. 4 indexed citations

Pradhan, Subrata, et al.. (2012). SST-1 Status and Plans. IEEE Transactions on Plasma Science. 40(3). 614–621. 15 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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