M. Birkner

982 total citations
24 papers, 769 citations indexed

About

M. Birkner is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Pulmonary and Respiratory Medicine. According to data from OpenAlex, M. Birkner has authored 24 papers receiving a total of 769 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Radiology, Nuclear Medicine and Imaging, 13 papers in Radiation and 11 papers in Pulmonary and Respiratory Medicine. Recurrent topics in M. Birkner's work include Advanced Radiotherapy Techniques (13 papers), Medical Imaging Techniques and Applications (8 papers) and HER2/EGFR in Cancer Research (8 papers). M. Birkner is often cited by papers focused on Advanced Radiotherapy Techniques (13 papers), Medical Imaging Techniques and Applications (8 papers) and HER2/EGFR in Cancer Research (8 papers). M. Birkner collaborates with scholars based in Germany, United States and United Kingdom. M. Birkner's co-authors include M. Alber, Fridtjof Nüsslin, Di Yan, Matthias Söhn, Jian Liang, Frederik Wenz, Frank Lohr, Hansjoerg Wertz, Volker Steil and W. Laub and has published in prestigious journals such as Journal of Clinical Oncology, Cancer Research and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

M. Birkner

24 papers receiving 743 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Birkner Germany 15 519 501 382 164 155 24 769
Ellen M. Kerkhof Netherlands 14 939 1.8× 839 1.7× 585 1.5× 94 0.6× 140 0.9× 25 1.2k
Tarek Shouman Egypt 9 954 1.8× 606 1.2× 825 2.2× 102 0.6× 240 1.5× 21 1.3k
Yibao Zhang China 17 424 0.8× 372 0.7× 290 0.8× 61 0.4× 160 1.0× 68 741
Poonam Yadav United States 15 481 0.9× 533 1.1× 376 1.0× 151 0.9× 132 0.9× 80 856
Hubert S. Gabryś Switzerland 10 286 0.6× 394 0.8× 346 0.9× 99 0.6× 119 0.8× 26 651
Geert Bosmans Netherlands 18 723 1.4× 1.0k 2.0× 855 2.2× 150 0.9× 94 0.6× 28 1.4k
Gert O. De Meerleer Belgium 14 434 0.8× 375 0.7× 657 1.7× 50 0.3× 96 0.6× 17 883
D Khullar United States 8 280 0.5× 651 1.3× 295 0.8× 56 0.3× 175 1.1× 15 771
K.H. Shin United States 7 293 0.6× 218 0.4× 513 1.3× 139 0.8× 74 0.5× 14 698
Tingliang Zhuang United States 13 360 0.7× 451 0.9× 367 1.0× 41 0.3× 171 1.1× 53 718

Countries citing papers authored by M. Birkner

Since Specialization
Citations

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

Fields of papers citing papers by M. Birkner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Birkner

This figure shows the co-authorship network connecting the top 25 collaborators of M. Birkner. A scholar is included among the top collaborators of M. Birkner 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 M. Birkner. M. Birkner 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.
Birkner, M., et al.. (2023). Real-world use of a deep convolutional neural network to assist in the diagnosis of pyoderma gangrenosum. JAAD Case Reports. 38. 8–10. 5 indexed citations
2.
Birkner, M., et al.. (2022). Computer-Assisted Differential Diagnosis of Pyoderma Gangrenosum and Venous Ulcers with Deep Neural Networks. Journal of Clinical Medicine. 11(23). 7103–7103. 12 indexed citations
3.
Birkner, M., et al.. (2011). Evaluation of a 2D detector array for patient-specific VMAT QA with different setups. Physics in Medicine and Biology. 56(22). 7163–7177. 43 indexed citations
4.
Lorenz, Friedlieb, Lutz Mueller, M. Birkner, et al.. (2010). Experimental validation of a commercial 3D dose verification system for intensity-modulated arc therapies. Physics in Medicine and Biology. 55(19). 5619–5633. 57 indexed citations
5.
Rugo, Hope S., Peter A. Kaufman, Elizabeth Tan-Chiu, et al.. (2009). Survival of patients with HER2+ metastatic breast cancer and use of trastuzumab following progression: analysis of RegistHER.. Cancer Research. 69(2_Supplement). 3142–3142. 7 indexed citations
6.
Krop, IE, et al.. (2009). A phase I study of weekly dosing of trastuzumab-DM1 (T-DM1) in patients with advanced HER2+ breast cancer.. Cancer Research. 69(2_Supplement). 3136–3136. 12 indexed citations
7.
Vogel, C. L., Howard A. Burris, Steven Limentani, et al.. (2009). A phase II study of trastuzumab-DM1 (T-DM1), a HER2 antibody-drug conjugate (ADC), in patients (pts) with HER2+ metastatic breast cancer (MBC): Final results. Journal of Clinical Oncology. 27(15_suppl). 1017–1017. 50 indexed citations
8.
Söhn, Matthias, M. Birkner, Jian Wang, et al.. (2008). Model‐independent, multimodality deformable image registration by local matching of anatomical features and minimization of elastic energy. Medical Physics. 35(3). 866–878. 32 indexed citations
9.
Beeram, M., H. A. Burris, Shanu Modi, et al.. (2008). A phase I study of trastuzumab-DM1 (T-DM1), a first-in-class HER2 antibody-drug conjugate (ADC), in patients (pts) with advanced HER2+ breast cancer (BC). Journal of Clinical Oncology. 26(15_suppl). 1028–1028. 36 indexed citations
10.
Birkner, M., et al.. (2007). Analysis of the rigid and deformable component of setup inaccuracies on portal images in head and neck radiotherapy. Physics in Medicine and Biology. 52(18). 5721–5733. 12 indexed citations
11.
12.
Söhn, Matthias, M. Birkner, Di Yan, & M. Alber. (2005). Modelling individual geometric variation based on dominant eigenmodes of organ deformation: implementation and evaluation. Physics in Medicine and Biology. 50(24). 5893–5908. 88 indexed citations
13.
Ganswindt, Ute, Frank Paulsen, Stefan Corvin, et al.. (2005). Intensity modulated radiotherapy for high risk prostate cancer based on sentinel node SPECT imaging for target volume definition. BMC Cancer. 5(1). 91–91. 27 indexed citations
14.
15.
Alber, M., et al.. (2005). Robust treatment planning for intensity modulated radiotherapy of prostate cancer based on coverage probabilities. Radiotherapy and Oncology. 78(1). 27–35. 83 indexed citations
16.
Alber, M., et al.. (2004). Treatment simulation approaches for the estimation of the distributions of treatment quality parameters generated by geometrical uncertainties. Physics in Medicine and Biology. 49(24). 5475–5488. 23 indexed citations
17.
Birkner, M., Di Yan, M. Alber, Jian Liang, & Fridtjof Nüsslin. (2003). Adapting inverse planning to patient and organ geometrical variation: algorithm and implementation. Medical Physics. 30(10). 2822–2831. 119 indexed citations
18.
Alber, M., et al.. (2002). Tools for the analysis of dose optimization: II. Sensitivity analysis. Physics in Medicine and Biology. 47(19). N265–N270. 25 indexed citations
19.
Yan, Di, et al.. (2001). Improvement in dose escalation using off-line & on-line image feedback in the intensity modulated beam design for prostate cancer treatment. International Journal of Radiation Oncology*Biology*Physics. 51(3). 91–92. 2 indexed citations
20.
Laub, W., M. Alber, M. Birkner, & Fridtjof Nüsslin. (2000). Monte Carlo dose computation for IMRT optimization*. Physics in Medicine and Biology. 45(7). 1741–1754. 53 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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