Rickmer Braren

7.2k total citations · 3 hit papers
131 papers, 3.5k citations indexed

About

Rickmer Braren is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Artificial Intelligence. According to data from OpenAlex, Rickmer Braren has authored 131 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 72 papers in Radiology, Nuclear Medicine and Imaging, 34 papers in Oncology and 18 papers in Artificial Intelligence. Recurrent topics in Rickmer Braren's work include Radiomics and Machine Learning in Medical Imaging (35 papers), Pancreatic and Hepatic Oncology Research (25 papers) and MRI in cancer diagnosis (24 papers). Rickmer Braren is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (35 papers), Pancreatic and Hepatic Oncology Research (25 papers) and MRI in cancer diagnosis (24 papers). Rickmer Braren collaborates with scholars based in Germany, United Kingdom and United States. Rickmer Braren's co-authors include Georgios Kaissis, Marcus R. Makowski, Daniel Rückert, Huiqing Hu, Ernst J. Rummeny, Daniel Rueckert, Yung Hae Kim, Friederike Jungmann, Alexander Ziller and Jens T. Siveke and has published in prestigious journals such as Nature Medicine, SHILAP Revista de lepidopterología and The Journal of Cell Biology.

In The Last Decade

Rickmer Braren

121 papers receiving 3.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Secure, privacy-preserving and federated machine learning in medical imaging

2020 678 citations Georgios Kaissis, Marcus R. Makowski et al. profile →
End-to-end privacy preserving deep learning on multi-institutional medical imaging

2021 255 citations Georgios Kaissis, Alexander Ziller et al. profile →
Evaluation and mitigation of the limitations of large language models in clinical decision-making

2024 200 citations Paul Hager, Friederike Jungmann et al. Nature Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rickmer Braren Germany	29	1.2k	955	573	571	527	131	3.5k
Jun Xia China	25	2.0k 1.7×	1.2k 1.3×	355 0.6×	523 0.9×	232 0.4×	124	4.0k
Jason Hipp United States	24	849 0.7×	1.2k 1.2×	455 0.8×	915 1.6×	188 0.4×	58	2.9k
Alexander T. Pearson United States	32	1.2k 1.0×	1.2k 1.3×	1.3k 2.3×	732 1.3×	318 0.6×	159	4.1k
Faisal Mahmood United States	24	1.3k 1.1×	1.9k 2.0×	365 0.6×	428 0.7×	296 0.6×	78	3.5k
Saeed Hassanpour United States	24	751 0.6×	926 1.0×	576 1.0×	290 0.5×	296 0.6×	80	2.1k
Tiffany Chen United States	22	801 0.7×	1.1k 1.2×	368 0.6×	313 0.5×	215 0.4×	63	2.8k
Rivka R. Colen United States	34	2.1k 1.8×	653 0.7×	535 0.9×	450 0.8×	489 0.9×	138	4.2k
David J. Foran United States	30	795 0.7×	1.2k 1.2×	312 0.5×	517 0.9×	464 0.9×	110	3.1k
Ming Y. Lu United States	33	1.4k 1.1×	1.5k 1.6×	378 0.7×	1.3k 2.3×	207 0.4×	76	4.7k
Drew F. K. Williamson United States	18	1.1k 0.9×	1.5k 1.5×	270 0.5×	372 0.7×	207 0.4×	42	2.8k

Countries citing papers authored by Rickmer Braren

Since Specialization

Citations

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

Fields of papers citing papers by Rickmer Braren

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rickmer Braren

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

All Works

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20 of 20 papers shown

Huang, Wenqi, Rickmer Braren, Daniel Rueckert, et al.. (2025). PISCO: Self-supervised k-space regularization for improved neural implicit k-space representations of dynamic MRI. Medical Image Analysis. 109. 103890–103890.

Pelka, Obioma, Matthias May, Andreas Bucher, et al.. (2025). Democratizing AI in Healthcare with Open Medical Inference (OMI): Protocols, Data Exchange, and AI Integration. RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. 198(2). 173–184.

Weiss, Kilian, Yoo Jin Lee, Thomas Amthor, et al.. (2024). Accelerated liver water T₁ mapping using single‐shot continuous inversion‐recovery spiral imaging. NMR in Biomedicine. 37(5). e5097–e5097. 4 indexed citations

Zimmer, Veronika A., et al.. (2024). Using UK Biobank data to establish population-specific atlases from whole body MRI. SHILAP Revista de lepidopterología. 4(1). 237–237. 2 indexed citations

Zimmer, Veronika A., et al.. (2024). Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction. Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition. 1 indexed citations

Hager, Paul, Friederike Jungmann, Robbie Holland, et al.. (2024). Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nature Medicine. 30(9). 2613–2622. 200 indexed citations breakdown →

Arasteh, Soroosh Tayebi, Alexander Ziller, Christiane Kühl, et al.. (2024). Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging. SHILAP Revista de lepidopterología. 4(1). 46–46. 16 indexed citations

Sauter, Andreas, Roland Proksa, Stefan Reischl, et al.. (2023). CT-Guided Liver Biopsy: Evaluation of Spectral Data From Dual-Layer Detector CT for Improved Lesion Detection. CardioVascular and Interventional Radiology. 46(11). 1621–1631. 1 indexed citations

Gassert, Florian T., Felix G. Gassert, Yannik Leonhardt, et al.. (2022). Associations between Bone Mineral Density and Longitudinal Changes of Vertebral Bone Marrow and Paraspinal Muscle Composition Assessed Using MR-Based Proton Density Fat Fraction and T2* Maps in Patients with and without Osteoporosis. Diagnostics. 12(10). 2467–2467. 9 indexed citations

10.

Lange, Sebastian, H. Carlo Maurer, Kenneth P. Olive, et al.. (2021). PALLD mutation in a European family conveys a stromal predisposition for familial pancreatic cancer. JCI Insight. 6(8). 7 indexed citations

11.

Burian, Egon, Olga Prokopchuk, Ulrich Nitsche, et al.. (2021). MRI-Determined Psoas Muscle Fat Infiltration Correlates with Severity of Weight Loss during Cancer Cachexia. Cancers. 13(17). 4433–4433. 10 indexed citations

12.

Burian, Egon, Friederike Jungmann, Georgios Kaissis, et al.. (2020). Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort. Journal of Clinical Medicine. 9(5). 1514–1514. 48 indexed citations

13.

Lohöfer, Fabian, Georgios Kaissis, Daniela Franz, et al.. (2019). Acceleration of chemical shift encoding-based water fat MRI for liver proton density fat fraction and T2* mapping using compressed sensing. PLoS ONE. 14(11). e0224988–e0224988. 11 indexed citations

14.

Sauter, Andreas, Felix K. Kopp, Daniela Münzel, et al.. (2018). Accuracy of iodine quantification in dual-layer spectral CT: Influence of iterative reconstruction, patient habitus and tube parameters. European Journal of Radiology. 102. 83–88. 51 indexed citations

15.

Sauter, Andreas, Daniela Muenzel, Julia Dangelmaier, et al.. (2018). Dual-layer spectral computed tomography: Virtual non-contrast in comparison to true non-contrast images. European Journal of Radiology. 104. 108–114. 90 indexed citations

16.

Dangelmaier, Julia, Benedikt J. Schwaiger, Alexandra S. Gersing, et al.. (2018). Dual layer computed tomography: Reduction of metal artefacts from posterior spinal fusion using virtual monoenergetic imaging. European Journal of Radiology. 105. 195–203. 19 indexed citations

17.

Grüner, Barbara M., I. Winkelmann, Annette Feuchtinger, et al.. (2016). Modeling Therapy Response and Spatial Tissue Distribution of Erlotinib in Pancreatic Cancer. Molecular Cancer Therapeutics. 15(5). 1145–1152. 22 indexed citations

18.

Heid, Irina, Katja Steiger, Marija Trajkovic‐Arsic, et al.. (2016). Co-clinical Assessment of Tumor Cellularity in Pancreatic Cancer. Clinical Cancer Research. 23(6). 1461–1470. 57 indexed citations

19.

Groß, Claudia, Katja Steiger, Irina Heid, et al.. (2015). Model Matters: Differences in Orthotopic Rat Hepatocellular Carcinoma Physiology Determine Therapy Response to Sorafenib. Clinical Cancer Research. 21(19). 4440–4450. 24 indexed citations

20.

Lee, Misu, Tobias Wiedemann, Claudia Groß, et al.. (2015). Targeting PI3K/mTOR Signaling Displays Potent Antitumor Efficacy against Nonfunctioning Pituitary Adenomas. Clinical Cancer Research. 21(14). 3204–3215. 48 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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