Michael Püsken

828 total citations
17 papers, 436 citations indexed

About

Michael Püsken is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Michael Püsken has authored 17 papers receiving a total of 436 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Radiology, Nuclear Medicine and Imaging, 7 papers in Pulmonary and Respiratory Medicine and 5 papers in Biomedical Engineering. Recurrent topics in Michael Püsken's work include Radiomics and Machine Learning in Medical Imaging (7 papers), Medical Imaging Techniques and Applications (5 papers) and Advanced X-ray and CT Imaging (5 papers). Michael Püsken is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (7 papers), Medical Imaging Techniques and Applications (5 papers) and Advanced X-ray and CT Imaging (5 papers). Michael Püsken collaborates with scholars based in Germany, Switzerland and Netherlands. Michael Püsken's co-authors include David Maintz, Bettina Baeßler, Mohammad Hosein Rezazade Mehrizi, Stephanie Sauer, Roman Kloeckner, Daniel Pinto dos Santos, Aline Mähringer‐Kunz, Thomas Dratsch, Christian Vahlhaus and Susanne Wienbeck and has published in prestigious journals such as Scientific Reports, Radiology and American Journal of Roentgenology.

In The Last Decade

Michael Püsken

17 papers receiving 420 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Püsken Germany 9 321 119 105 100 85 17 436
Christian V. Guthier United States 9 282 0.9× 71 0.6× 61 0.6× 146 1.5× 83 1.0× 27 416
Reabal Najjar Australia 4 215 0.7× 116 1.0× 80 0.8× 50 0.5× 162 1.9× 6 390
Yazdan Salimi Switzerland 16 541 1.7× 282 2.4× 87 0.8× 97 1.0× 37 0.4× 69 636
Dong Joo Rhee United States 15 467 1.5× 170 1.4× 85 0.8× 167 1.7× 45 0.5× 43 660
Pritam Mukherjee United States 12 316 1.0× 65 0.5× 149 1.4× 88 0.9× 80 0.9× 39 484
J. van der Stoep Netherlands 7 451 1.4× 122 1.0× 61 0.6× 308 3.1× 27 0.3× 13 613
Fredrik Löfman Sweden 6 264 0.8× 81 0.7× 115 1.1× 73 0.7× 70 0.8× 9 422
Ai Dozen Japan 10 203 0.6× 46 0.4× 134 1.3× 41 0.4× 115 1.4× 12 483
Kanto Shozu Japan 10 203 0.6× 46 0.4× 134 1.3× 41 0.4× 115 1.4× 12 484
Florian Prayer Austria 11 413 1.3× 127 1.1× 100 1.0× 240 2.4× 36 0.4× 36 646

Countries citing papers authored by Michael Püsken

Since Specialization
Citations

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

Fields of papers citing papers by Michael Püsken

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Püsken

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

All Works

17 of 17 papers shown
1.
Fassunke, Jana, Michael Püsken, Elke Binot, et al.. (2023). Durable Response With Sequential Tyrosine Kinase Inhibitor Treatment in a Patient With ROS1 Fusion–Positive Pancreatic Adenocarcinoma: A Case Report. JCO Precision Oncology. 7(7). e2200467–e2200467. 2 indexed citations
2.
Dratsch, Thomas, Mohammad Hosein Rezazade Mehrizi, Roman Kloeckner, et al.. (2023). Automation Bias in Mammography: The Impact of Artificial Intelligence BI-RADS Suggestions on Reader Performance. Radiology. 307(4). e222176–e222176. 125 indexed citations
3.
Fischer, Uwe, Felix Diekmann, Thomas H. Helbich, et al.. (2023). Einsatz der kontrastmittelverstärkten Mammographie in der Brustkrebsdiagnostik. Die Radiologie. 63(12). 916–924. 2 indexed citations
4.
Schömig‐Markiefka, Birgid, Graeme M. Campbell, Michael Püsken, et al.. (2022). Correlation of CT-data derived from multiparametric dual-layer CT-maps with immunohistochemical biomarkers in invasive breast carcinomas. European Journal of Radiology. 156. 110544–110544. 10 indexed citations
5.
Blümcke, Britta, et al.. (2022). Long‑term survival of a BRCA2 mutation carrier following second ovarian cancer relapse using PARPi therapy: A case report. Molecular and Clinical Oncology. 17(3). 137–137. 1 indexed citations
6.
Iuga, Andra-Iza, Liliana Caldeira, Simon Lennartz, et al.. (2021). Automated mapping and N-Staging of thoracic lymph nodes in contrast-enhanced CT scans of the chest using a fully convolutional neural network. European Journal of Radiology. 139. 109718–109718. 6 indexed citations
7.
Iuga, Andra-Iza, Tom Brosch, Tobias Klinder, et al.. (2021). Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks. BMC Medical Imaging. 21(1). 69–69. 15 indexed citations
8.
Suárez, Isabelle, Melanie Stecher, Clara Lehmann, et al.. (2020). Plasma interferon-γ-inducible protein 10 (IP-10) levels correlate with disease severity and paradoxical reactions in extrapulmonary tuberculosis. Infection. 49(3). 437–445. 5 indexed citations
9.
Hokamp, Nils Große, Stefan Haneder, Susanne Steinhauser, et al.. (2020). Virtual mono-energetic images and iterative image reconstruction: abdominal vessel imaging in the era of spectral detector CT. Clinical Radiology. 75(8). 641.e9–641.e18. 10 indexed citations
10.
Holz, Jasmin A., Hatem Alkadhi, Kai Roman Laukamp, et al.. (2020). Quantitative accuracy of virtual non-contrast images derived from spectral detector computed tomography: an abdominal phantom study. Scientific Reports. 10(1). 21575–21575. 17 indexed citations
11.
Iuga, Andra-Iza, Tom Brosch, Rafael Wiemker, et al.. (2020). Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks. 44–44. 4 indexed citations
12.
Krug, B., Martin Hellmich, Stefan Krämer, et al.. (2016). Vacuum-assisted breast biopsies (VAB) carried out on an open 1.0 T MR imager: Influence of patient and target characteristics on the procedural and clinical results. European Journal of Radiology. 85(6). 1157–1166. 5 indexed citations
13.
14.
Moltz, Jan Hendrik, Lars Bornemann, Jan‐Martin Kuhnigk, et al.. (2009). Advanced Segmentation Techniques for Lung Nodules, Liver Metastases, and Enlarged Lymph Nodes in CT Scans. IEEE Journal of Selected Topics in Signal Processing. 3(1). 122–134. 115 indexed citations
15.
Moltz, Jan Hendrik, Lars Bornemann, Volker Dicken, et al.. (2009). 3D contour based local manual correction of tumor segmentations in CT scans. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7259. 72593L–72593L. 7 indexed citations
16.
Amarteifio, Erick, et al.. (2008). Detection of gastrointestinal bleeding by using multislice computed tomography—acute and chronic hemorrhages. Clinical Imaging. 32(1). 1–5. 9 indexed citations
17.
Seifarth, Harald, Susanne Wienbeck, Michael Püsken, et al.. (2007). Optimal Systolic and Diastolic Reconstruction Windows for Coronary CT Angiography Using Dual-Source CT. American Journal of Roentgenology. 189(6). 1317–1323. 84 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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