Ravi Managuli

1.3k total citations
40 papers, 859 citations indexed

About

Ravi Managuli is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Biomedical Engineering. According to data from OpenAlex, Ravi Managuli has authored 40 papers receiving a total of 859 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Radiology, Nuclear Medicine and Imaging, 17 papers in Computer Vision and Pattern Recognition and 12 papers in Biomedical Engineering. Recurrent topics in Ravi Managuli's work include Ultrasound Imaging and Elastography (18 papers), Medical Image Segmentation Techniques (10 papers) and Photoacoustic and Ultrasonic Imaging (10 papers). Ravi Managuli is often cited by papers focused on Ultrasound Imaging and Elastography (18 papers), Medical Image Segmentation Techniques (10 papers) and Photoacoustic and Ultrasonic Imaging (10 papers). Ravi Managuli collaborates with scholars based in United States, South Korea and Japan. Ravi Managuli's co-authors include Chulhong Kim, Seungwan Jeon, Wonseok Choi, Eun-Yeong Park, Seonghee Cho, Sampa Misra, Yongmin Kim, Vijay Shamdasani, Yang Mo Yoo and Ben Seiyon Lee and has published in prestigious journals such as Cancer Research, Scientific Reports and The Journal of Urology.

In The Last Decade

Ravi Managuli

38 papers receiving 834 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ravi Managuli United States 16 516 467 286 119 116 40 859
Nicole V. Ruiter Germany 20 691 1.3× 952 2.0× 270 0.9× 163 1.4× 213 1.8× 138 1.3k
Mohamed Almekkawy United States 14 412 0.8× 365 0.8× 163 0.6× 155 1.3× 115 1.0× 87 859
Rainer Stotzka Germany 12 158 0.3× 202 0.4× 33 0.1× 87 0.7× 96 0.8× 52 480
Ming Yuchi China 12 232 0.4× 180 0.4× 54 0.2× 115 1.0× 64 0.6× 89 538
Martino Alessandrini Belgium 16 211 0.4× 419 0.9× 53 0.2× 158 1.3× 36 0.3× 45 728
Binjie Qin China 14 228 0.4× 351 0.8× 31 0.1× 226 1.9× 158 1.4× 37 759
Phani Chinchapatnam United Kingdom 15 148 0.3× 223 0.5× 122 0.4× 45 0.4× 10 0.1× 31 928
K. Heath Martin United States 17 548 1.1× 409 0.9× 77 0.3× 514 4.3× 40 0.3× 42 1.2k

Countries citing papers authored by Ravi Managuli

Since Specialization
Citations

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

Fields of papers citing papers by Ravi Managuli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ravi Managuli

This figure shows the co-authorship network connecting the top 25 collaborators of Ravi Managuli. A scholar is included among the top collaborators of Ravi Managuli 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 Ravi Managuli. Ravi Managuli 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.
Cho, Yoon‐Jae, Sampa Misra, Ravi Managuli, et al.. (2024). Attention-based Fusion Network for Breast Cancer Segmentation and Classification Using Multi-modal Ultrasound Images. Ultrasound in Medicine & Biology. 51(3). 568–577. 7 indexed citations
3.
Misra, Sampa, et al.. (2022). Deep learning‐based multimodal fusion network for segmentation and classification of breast cancers using B‐mode and elastography ultrasound images. Bioengineering & Translational Medicine. 8(6). e10480–e10480. 32 indexed citations
4.
Kim, Jeesu, Byullee Park, Jeonghoon Ha, et al.. (2021). Multiparametric Photoacoustic Analysis of Human Thyroid Cancers In Vivo. Cancer Research. 81(18). 4849–4860. 96 indexed citations
5.
Misra, Sampa, Seungwan Jeon, Ravi Managuli, et al.. (2021). Bi-Modal Transfer Learning for Classifying Breast Cancers via Combined B-Mode and Ultrasound Strain Imaging. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 69(1). 222–232. 43 indexed citations
6.
Cho, Seonghee, et al.. (2020). 3D PHOVIS: 3D photoacoustic visualization studio. Photoacoustics. 18. 100168–100168. 82 indexed citations
7.
Jeon, Seungwan, et al.. (2019). Real-time delay-multiply-and-sum beamforming with coherence factor for in vivo clinical photoacoustic imaging of humans. Photoacoustics. 15. 100136–100136. 107 indexed citations
8.
Barr, R. Graham & Ravi Managuli. (2018). A Clinical Study Comparing the Diagnostic Performance of Assist Strain Ratio Against Manual Strain Ratio in Ultrasound Breast Elastography. Ultrasound Quarterly. 35(1). 82–87. 3 indexed citations
9.
Morris, Michael, Christina Ring, Ravi Managuli, et al.. (2017). Feature analysis of ultrasound elastography image for quantitative assessment of cutaneous carcinoma. Skin Research and Technology. 24(2). 242–247. 8 indexed citations
10.
Jeon, Seungwan, Hyun Beom Song, Jae-Woo Kim, et al.. (2017). In Vivo Photoacoustic Imaging of Anterior Ocular Vasculature: A Random Sample Consensus Approach. Scientific Reports. 7(1). 4318–4318. 84 indexed citations
11.
Shamdasani, Vijay, et al.. (2009). Real-Time 3-D Ultrasound Scan Conversion Using a Multicore Processor. IEEE Transactions on Information Technology in Biomedicine. 13(4). 571–574. 8 indexed citations
12.
Shamdasani, Vijay, et al.. (2007). Research interface on a programmable ultrasound scanner. Ultrasonics. 48(3). 159–168. 20 indexed citations
13.
Yoo, Yang Mo, Ravi Managuli, & Yong Min Kim. (2007). New multi-volume rendering technique for three-dimensional power Doppler imaging. Ultrasonics. 46(4). 313–322. 3 indexed citations
14.
Kawabata, Kohei, Takashi Azuma, Hiroyuki Yoshikawa, et al.. (2006). P2D-1 High Contrast Ultrasound Imaging by Motion-Compensated Time-Averaging Method. 31. 1618–1621. 2 indexed citations
15.
Shamdasani, Vijay, et al.. (2005). Improving the visualization of 3D ultrasound data with 3D filtering. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5744. 455–455. 6 indexed citations
16.
Shamdasani, Vijay, et al.. (2004). Ultrasound Color-Flow Imaging on a Programmable System. IEEE Transactions on Information Technology in Biomedicine. 8(2). 191–199. 16 indexed citations
17.
Managuli, Ravi, et al.. (2003). Tomosynthesis‐based localization of radioactive seeds in prostate brachytherapy. Medical Physics. 30(12). 3135–3142. 42 indexed citations
18.
Shamdasani, Vijay, et al.. (2003). Fast Adaptive Unsharp Masking with Programmable Mediaprocessors. Journal of Digital Imaging. 16(2). 230–239. 7 indexed citations
19.
Yoo, Yang Mo, Ravi Managuli, & Yongmin Kim. (2003). Adaptive clutter filtering for ultrasound color flow imaging. Ultrasound in Medicine & Biology. 29(9). 1311–1320. 37 indexed citations
20.
Managuli, Ravi, et al.. (1998). <title>Efficient convolution algorithm for VLIW media processors</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 3655. 65–74. 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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