Suzan Vreemann

826 total citations
21 papers, 589 citations indexed

About

Suzan Vreemann is a scholar working on Radiology, Nuclear Medicine and Imaging, Pathology and Forensic Medicine and Artificial Intelligence. According to data from OpenAlex, Suzan Vreemann has authored 21 papers receiving a total of 589 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Pathology and Forensic Medicine and 7 papers in Artificial Intelligence. Recurrent topics in Suzan Vreemann's work include MRI in cancer diagnosis (12 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and AI in cancer detection (7 papers). Suzan Vreemann is often cited by papers focused on MRI in cancer diagnosis (12 papers), Radiomics and Machine Learning in Medical Imaging (7 papers) and AI in cancer detection (7 papers). Suzan Vreemann collaborates with scholars based in Netherlands, United States and United Kingdom. Suzan Vreemann's co-authors include Ritse M. Mann, Albert Gubern‐Mérida, Nico Karssemeijer, Peter Bult, Mehmet Ufuk Dalmış, Nico Karssemeijer, Jan van Zelst, Jonas Teuwen, Nicoline Hoogerbrugge and Roel Mus and has published in prestigious journals such as PLoS ONE, Radiology and Medical Physics.

In The Last Decade

Suzan Vreemann

20 papers receiving 579 citations

Peers

Suzan Vreemann
Jan van Zelst Netherlands
Chao You China
Georg Wengert Austria
Deepa Sheth United States
Brittany Z. Dashevsky United States
Katya M. Duvivier Netherlands
Kimberly M. Ray United States
David Spak United States
Daly Avendaño Italy
Stylianos Drisis Belgium
Jan van Zelst Netherlands
Suzan Vreemann
Citations per year, relative to Suzan Vreemann Suzan Vreemann (= 1×) peers Jan van Zelst

Countries citing papers authored by Suzan Vreemann

Since Specialization
Citations

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

Fields of papers citing papers by Suzan Vreemann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suzan Vreemann

This figure shows the co-authorship network connecting the top 25 collaborators of Suzan Vreemann. A scholar is included among the top collaborators of Suzan Vreemann 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 Suzan Vreemann. Suzan Vreemann 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.
Rier, Sophie, et al.. (2022). Interventional cardiac magnetic resonance imaging: current applications, technology readiness level, and future perspectives. Therapeutic Advances in Cardiovascular Disease. 16. 3375098504–3375098504. 7 indexed citations
2.
Vreemann, Suzan, Mehmet Ufuk Dalmış, Peter Bult, et al.. (2019). Amount of fibroglandular tissue FGT and background parenchymal enhancement BPE in relation to breast cancer risk and false positives in a breast MRI screening program. European Radiology. 29(9). 4678–4690. 27 indexed citations
3.
Dalmış, Mehmet Ufuk, Albert Gubern‐Mérida, Suzan Vreemann, et al.. (2019). Artificial Intelligence–Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI. Investigative Radiology. 54(6). 325–332. 92 indexed citations
4.
Mann, Ritse M., Jan van Zelst, Suzan Vreemann, & Roel Mus. (2019). Is Ultrafast or Abbreviated Breast MRI Ready for Prime Time?. Current Breast Cancer Reports. 11(1). 9–16. 19 indexed citations
5.
Zelst, Jan van, Suzan Vreemann, Albert Gubern‐Mérida, et al.. (2018). Multireader Study on the Diagnostic Accuracy of Ultrafast Breast Magnetic Resonance Imaging for Breast Cancer Screening. Investigative Radiology. 53(10). 579–586. 64 indexed citations
6.
Vreemann, Suzan, Jan van Zelst, Margrethe Schlooz-Vries, et al.. (2018). The added value of mammography in different age-groups of women with and without BRCA mutation screened with breast MRI. Breast Cancer Research. 20(1). 84–84. 30 indexed citations
7.
8.
Vreemann, Suzan, Albert Gubern‐Mérida, Peter Bult, et al.. (2018). The frequency of missed breast cancers in women participating in a high-risk MRI screening program. Breast Cancer Research and Treatment. 169(2). 323–331. 23 indexed citations
9.
Rodríguez‐Ruiz, Alejandro, Ruben E. van Engen, Koen Michielsen, et al.. (2018). How does wide-angle breast tomosynthesis depict calcifications in comparison to digital mammography? A retrospective observer study. 33–33. 3 indexed citations
10.
Dalmış, Mehmet Ufuk, Suzan Vreemann, Thijs Kooi, et al.. (2018). Fully automated detection of breast cancer in screening MRI using convolutional neural networks. Journal of Medical Imaging. 5(1). 1–1. 52 indexed citations
11.
Vreemann, Suzan, Alejandro Rodríguez‐Ruiz, Dominik Nickel, et al.. (2017). Compressed Sensing for Breast MRI: Resolving the Trade-Off Between Spatial and Temporal Resolution. Investigative Radiology. 52(10). 574–582. 45 indexed citations
12.
Rodríguez‐Ruiz, Alejandro, Steve Si Jia Feng, Jan van Zelst, et al.. (2017). Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes. Medical Physics. 44(6). 2161–2172. 9 indexed citations
13.
Mertzanidou, Thomy, John H. Hipwell, Sara Monteiro‐Reis, et al.. (2017). 3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging. Medical Physics. 44(3). 935–948. 15 indexed citations
14.
Vreemann, Suzan, Albert Gubern‐Mérida, Margrethe Schlooz-Vries, et al.. (2017). Influence of Risk Category and Screening Round on the Performance of an MR Imaging and Mammography Screening Program in Carriers of the BRCA Mutation and Other Women at Increased Risk. Radiology. 286(2). 443–451. 51 indexed citations
15.
Zelst, Jan van, Roel Mus, G.H. Woldringh, et al.. (2017). Surveillance of Women with the BRCA1 or BRCA2 Mutation by Using Biannual Automated Breast US, MR Imaging, and Mammography. Radiology. 285(2). 376–388. 51 indexed citations
16.
Rodríguez‐Ruiz, Alejandro, Jonas Teuwen, Suzan Vreemann, et al.. (2017). New reconstruction algorithm for digital breast tomosynthesis: better image quality for humans and computers. Acta Radiologica. 59(9). 1051–1059. 29 indexed citations
17.
Dalmış, Mehmet Ufuk, et al.. (2016). A fully automated system for quantification of background parenchymal enhancement in breast DCE-MRI. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9785. 97850L–97850L. 2 indexed citations
18.
Gubern‐Mérida, Albert, Suzan Vreemann, Robert Martí, et al.. (2015). Automated detection of breast cancer in false-negative screening MRI studies from women at increased risk. European Journal of Radiology. 85(2). 472–479. 21 indexed citations
19.
Dalmış, Mehmet Ufuk, Albert Gubern‐Mérida, Suzan Vreemann, et al.. (2015). A computer-aided diagnosis system for breast DCE-MRI at high spatiotemporal resolution. Medical Physics. 43(1). 84–94. 27 indexed citations
20.
Ahmed, Muneer, Bauke Anninga, Suzan Vreemann, et al.. (2015). Optimising magnetic sentinel lymph node biopsy in an in vivo porcine model. Nanomedicine Nanotechnology Biology and Medicine. 11(4). 993–1002. 8 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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