Massimo Mischi

7.4k total citations
368 papers, 5.2k citations indexed

About

Massimo Mischi is a scholar working on Biomedical Engineering, Radiology, Nuclear Medicine and Imaging and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Massimo Mischi has authored 368 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 195 papers in Biomedical Engineering, 141 papers in Radiology, Nuclear Medicine and Imaging and 94 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Massimo Mischi's work include Ultrasound Imaging and Elastography (89 papers), Ultrasound and Hyperthermia Applications (76 papers) and Photoacoustic and Ultrasonic Imaging (70 papers). Massimo Mischi is often cited by papers focused on Ultrasound Imaging and Elastography (89 papers), Ultrasound and Hyperthermia Applications (76 papers) and Photoacoustic and Ultrasonic Imaging (70 papers). Massimo Mischi collaborates with scholars based in Netherlands, United States and Belgium. Massimo Mischi's co-authors include Chiara Rabotti, Hessel Wijkstra, S. Guid Oei, Ruud J. G. van Sloun, J.W.M. Bergmans, M.P.J. Kuenen, Rogier R. Wildeboer, Arnoud W. Postema, M. J. Rooijakkers and Rik Vullings and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Massimo Mischi

337 papers receiving 5.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Massimo Mischi Netherlands 38 2.4k 1.7k 1.4k 1.1k 634 368 5.2k
Joel P. Felmlee United States 47 2.4k 1.0× 5.3k 3.2× 869 0.6× 634 0.6× 225 0.4× 182 8.1k
Luigi Landini Italy 38 881 0.4× 2.0k 1.2× 567 0.4× 1.5k 1.4× 77 0.1× 261 4.9k
Dietrich Grönemeyer Germany 31 507 0.2× 1.5k 0.9× 530 0.4× 1.6k 1.4× 530 0.8× 137 4.4k
Jacques Felblinger France 30 686 0.3× 1.7k 1.0× 419 0.3× 880 0.8× 179 0.3× 182 3.9k
Lei Chen China 31 820 0.3× 779 0.5× 648 0.5× 227 0.2× 80 0.1× 287 3.8k
Purang Abolmaesumi Canada 40 2.4k 1.0× 1.9k 1.2× 732 0.5× 390 0.4× 49 0.1× 313 5.6k
Jean‐Luc Gennisson France 51 6.6k 2.8× 6.6k 4.0× 580 0.4× 453 0.4× 126 0.2× 185 10.5k
Vincenzo Positano Italy 41 531 0.2× 1.5k 0.9× 426 0.3× 1.4k 1.3× 200 0.3× 305 5.1k
Brian S. Garra United States 36 4.2k 1.8× 5.2k 3.1× 814 0.6× 201 0.2× 170 0.3× 162 8.2k
Krishna S. Nayak United States 40 519 0.2× 3.6k 2.2× 293 0.2× 1.0k 1.0× 129 0.2× 219 5.8k

Countries citing papers authored by Massimo Mischi

Since Specialization
Citations

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

Fields of papers citing papers by Massimo Mischi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Massimo Mischi

This figure shows the co-authorship network connecting the top 25 collaborators of Massimo Mischi. A scholar is included among the top collaborators of Massimo Mischi 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 Massimo Mischi. Massimo Mischi 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.
Zizolfi, Brunella, Federica Nardelli, Virginia Foreste, et al.. (2025). Quantitative ultrasound measurement of uterine contractility in septate uterus vs. normal uteri: a multicenter prospective study. Fertility and Sterility. 124(4). 728–736.
2.
Turco, Simona, David Mills, Kirk Wallace, et al.. (2025). Three-Dimensional Shear-Wave Viscoelastographic Estimation by System Identification for Prostate Cancer Localization. Ultrasound in Medicine & Biology. 51(11). 2089–2098.
3.
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Mischi, Massimo, et al.. (2025). Speckle Denoising of Dynamic Contrast- Enhanced Ultrasound Using Low-Rank Tensor Decomposition. IEEE Transactions on Medical Imaging. 44(7). 2854–2867. 1 indexed citations
5.
Peri, Elisabetta, et al.. (2025). A personalized model and optimization strategy for estimating blood glucose concentrations from sweat measurements. Computer Methods and Programs in Biomedicine. 265. 108743–108743. 2 indexed citations
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Peri, Elisabetta, et al.. (2024). Estimation of the Number of Active Sweat Glands Through Discrete Sweat Sensing. Sensors. 24(22). 7187–7187.
9.
Konings, Constantijn, et al.. (2024). The correlation of urea and creatinine concentrations in sweat and saliva with plasma during hemodialysis: an observational cohort study. Clinical Chemistry and Laboratory Medicine (CCLM). 62(6). 1118–1125. 9 indexed citations
10.
Peri, Elisabetta, et al.. (2024). Measurement of Sweat Gland Activity by Sweat Sensing and Deep Learning. TU/e Research Portal. 1–6.
11.
Joshi, Rohan, et al.. (2024). Changes in Maternal Heart Rate Variability and Photoplethysmography Morphology after Corticosteroid Administration: A Prospective, Observational Study. Journal of Clinical Medicine. 13(8). 2442–2442. 1 indexed citations
12.
Muehlsteff, Jens, et al.. (2024). Pressure-Less Local Pulse Wave Speed Estimation in the Carotid Artery Using Ultrasound-Based Velocity Waveform Indices. TU/e Research Portal. 1–5. 1 indexed citations
13.
Bouwman, R. Arthur, et al.. (2023). An In-silico Study of Sex Differences in Carotid Hemodynamic Waveforms. Computing in cardiology. 1 indexed citations
14.
Turco, Simona, Panagiotis Kapetas, Ritse M. Mann, et al.. (2023). Spatiotemporal analysis of contrast-enhanced ultrasound for differentiating between malignant and benign breast lesions. European Radiology. 34(7). 4764–4773. 1 indexed citations
15.
Turco, Simona, Thodsawit Tiyarattanachai, John R. Eisenbrey, et al.. (2022). Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 69(5). 1670–1681. 29 indexed citations
16.
Barentsz, Jelle O., et al.. (2021). Deep learning for prostate and zonal segmentation on a multicenter MRI dataset. European Urology Open Science. 33. S141–S142. 1 indexed citations
17.
Hyun, Dongwoon, Alycen Wiacek, Sobhan Goudarzi, et al.. (2021). Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets. IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control. 68(12). 3466–3483. 56 indexed citations
18.
Sloun, Ruud J. G. van, Simona Turco, Hessel Wijkstra, et al.. (2020). Blood flow patterns estimation in the left ventricle with low-rate 2D and 3D dynamic contrast-enhanced ultrasound. Computer Methods and Programs in Biomedicine. 198. 105810–105810. 5 indexed citations
19.
Postema, Arnoud W., Tamerlan Saidov, Libertario Demi, et al.. (2015). 3D surface-based registration of ultrasound and histology in prostate cancer imaging. Computerized Medical Imaging and Graphics. 47. 29–39. 17 indexed citations
20.
Mischi, Massimo, A.H.M. Jansen, A.A.C.M. Kalker, & H.H.M. Korsten. (2004). A New Ultrasound Dilution Method for EF Quantification. Journal of the American Society of Echocardiography. 17(5). 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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