Robert Grimm

4.4k total citations · 1 hit paper
148 papers, 3.2k citations indexed

About

Robert Grimm is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Robert Grimm has authored 148 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 122 papers in Radiology, Nuclear Medicine and Imaging, 29 papers in Pulmonary and Respiratory Medicine and 23 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Robert Grimm's work include MRI in cancer diagnosis (85 papers), Radiomics and Machine Learning in Medical Imaging (56 papers) and Advanced MRI Techniques and Applications (51 papers). Robert Grimm is often cited by papers focused on MRI in cancer diagnosis (85 papers), Radiomics and Machine Learning in Medical Imaging (56 papers) and Advanced MRI Techniques and Applications (51 papers). Robert Grimm collaborates with scholars based in Germany, China and United States. Robert Grimm's co-authors include Kai Tobias Block, Hersh Chandarana, Daniel K. Sodickson, Li Feng, Ricardo Otazo, Sungheon Kim, Leon Axel, Jian Xu, Caixia Fu and Joachim Hornegger and has published in prestigious journals such as PLoS ONE, Scientific Reports and Radiology.

In The Last Decade

Robert Grimm

134 papers receiving 3.1k 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.

Golden‐angle radial sparse parallel MRI: Combination of compressed sensing, parallel imaging, and golden‐angle radial sampling for fast and flexible dynamic volumetric MRI

2013 531 citations Robert Grimm, Kai Tobias Block et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Robert Grimm Germany	30	2.6k	582	433	302	298	148	3.2k
Jingfei Ma United States	30	2.1k 0.8×	428 0.7×	149 0.3×	282 0.9×	123 0.4×	121	3.1k
Marc Van Cauteren Japan	29	4.1k 1.6×	867 1.5×	571 1.3×	537 1.8×	175 0.6×	91	4.9k
Steven Sourbron United Kingdom	34	3.4k 1.3×	591 1.0×	134 0.3×	248 0.8×	296 1.0×	129	4.2k
Winfried A. Willinek Germany	36	2.0k 0.8×	767 1.3×	210 0.5×	460 1.5×	270 0.9×	118	3.6k
Brian M. Dale United States	26	1.9k 0.7×	243 0.4×	225 0.5×	314 1.0×	253 0.8×	79	2.5k
Henrik J. Michaely Germany	37	2.7k 1.0×	1.1k 1.9×	255 0.6×	575 1.9×	142 0.5×	144	3.9k
Eric P. Visser Netherlands	29	2.2k 0.8×	694 1.2×	143 0.3×	221 0.7×	84 0.3×	80	3.0k
Lale Umutlu Germany	38	3.4k 1.3×	1.3k 2.2×	169 0.4×	751 2.5×	199 0.7×	273	5.2k
Christian Plathow Germany	31	1.6k 0.6×	1.2k 2.0×	638 1.5×	168 0.6×	72 0.2×	63	2.8k
Munenobu Nogami Japan	28	1.3k 0.5×	1.0k 1.7×	483 1.1×	148 0.5×	64 0.2×	91	2.2k

Countries citing papers authored by Robert Grimm

Since Specialization

Citations

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

Fields of papers citing papers by Robert Grimm

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Robert Grimm

This figure shows the co-authorship network connecting the top 25 collaborators of Robert Grimm. A scholar is included among the top collaborators of Robert Grimm 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 Robert Grimm. Robert Grimm 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

Engel, Hannes, Caroline Wilpert, August Sigle, et al.. (2025). Multi-Center Benchmarking of a Commercially Available Artificial Intelligence Algorithm for Prostate Imaging Reporting and Data System (PI-RADS) Score Assignment and Lesion Detection in Prostate MRI. Cancers. 17(5). 815–815. 1 indexed citations

Lee, In Sook, You Seon Song, Young Jin Choi, et al.. (2025). Dynamic Contrast-Enhanced MRI in the Evaluation of Soft Tissue Tumors and Tumor-Like Lesions: Technical Principles and Clinical Applications. Korean Journal of Radiology. 26(11). 1054–1054.

Wenzel, M, et al.. (2025). Initial results of an AI-guided evaluation of CE breast MRI. 5. 100057–100057.

Engel, Hannes, Robert Grimm, Heinrich von Busch, et al.. (2025). Diagnostic performance of a fully automated AI algorithm for lesion detection and PI-RADS classification in patients with suspected prostate cancer. La radiologia medica. 130(7). 1039–1049.

Klimeš, Filip, Agilo Luitger Kern, Andreas Voskrebenzev, et al.. (2024). Free-breathing 3D phase-resolved functional lung MRI vs breath-hold hyperpolarized 129Xe ventilation MRI in patients with chronic obstructive pulmonary disease and healthy volunteers. European Radiology. 35(2). 943–956. 4 indexed citations

Zhang, Shaoting, Caixia Fu, Robert Grimm, et al.. (2024). Predicting disease-free survival in locally advanced rectal cancer using a prognostic model based on pretreatment b-value threshold map and postoperative pathologic features. Japanese Journal of Radiology. 43(2). 236–246.

Busch, Heinrich von, Robert Grimm, Henkjan Huisman, et al.. (2024). Deep Learning–based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets. Radiology Artificial Intelligence. 6(5). e230521–e230521. 3 indexed citations

Nissan, Noam, et al.. (2023). Ultrafast DCE-MRI for discriminating pregnancy-associated breast cancer lesions from lactation related background parenchymal enhancement. European Radiology. 33(11). 8122–8131. 9 indexed citations

Engel, Hannes, Christopher L. Schlett, Robert Grimm, et al.. (2023). Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: Performance of a fully automated AI‐algorithm for lesion detection and classification. The Prostate. 83(9). 871–878. 9 indexed citations

10.

Li, Jiao, Ling Yuan, Qin Wang, et al.. (2023). Intravoxel incoherent motion imaging to assess the acute effects of moderate‐intensity continuous training and high‐intensity interval training on thigh muscles. NMR in Biomedicine. 37(1). e5045–e5045.

11.

Zhan, Chenao, et al.. (2023). Histogram analysis of breast diffusion kurtosis imaging: a comparison between readout-segmented and single-shot echo-planar imaging sequence. Quantitative Imaging in Medicine and Surgery. 13(2). 735–746. 5 indexed citations

12.

Dyke, Jonathan P., Andreas Voskrebenzev, Jens Vogel‐Claussen, et al.. (2023). Assessment of lung ventilation of premature infants with bronchopulmonary dysplasia at 1.5 Tesla using phase-resolved functional lung magnetic resonance imaging. Pediatric Radiology. 53(6). 1076–1084. 7 indexed citations

13.

Bortolotto, Chandra, Giulia Maria Stella, Giovanna Nicora, et al.. (2022). Correlation between PD-L1 Expression of Non-Small Cell Lung Cancer and Data from IVIM-DWI Acquired during Magnetic Resonance of the Thorax: Preliminary Results. Cancers. 14(22). 5634–5634. 6 indexed citations

14.

Song, Sung Eun, Ok Hee Woo, Kyu Ran Cho, et al.. (2020). Simultaneous Multislice Readout‐Segmented Echo Planar Imaging for Diffusion‐Weighted MRI in Patients With Invasive Breast Cancers. Journal of Magnetic Resonance Imaging. 53(4). 1108–1115. 21 indexed citations

15.

Granata, Vincenza, Roberta Fusco, Sergio Venanzio Setola, et al.. (2019). Diffusion kurtosis imaging and conventional diffusion weighted imaging to assess electrochemotherapy response in locally advanced pancreatic cancer. Radiology and Oncology. 53(1). 15–24. 20 indexed citations

16.

Kim, Jin You, Jin Joo Kim, Lee Hwangbo, et al.. (2019). Diffusion-weighted MRI of estrogen receptor-positive, HER2-negative, node-negative breast cancer: association between intratumoral heterogeneity and recurrence risk. European Radiology. 30(1). 66–76. 29 indexed citations

17.

Zhao, Qiufeng, Caixia Fu, Qianming Bai, et al.. (2018). Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging. European Radiology. 29(5). 2535–2544. 66 indexed citations

18.

Kim, Jin You, Jin Joo Kim, Ji Won Lee, et al.. (2018). Risk stratification of ductal carcinoma in situ using whole-lesion histogram analysis of the apparent diffusion coefficient. European Radiology. 29(2). 485–493. 15 indexed citations

19.

Yoon, Jeong Hee, Jeong Min Lee, Mi Hye Yu, et al.. (2017). Evaluation of Transient Motion During Gadoxetic Acid–Enhanced Multiphasic Liver Magnetic Resonance Imaging Using Free-Breathing Golden-Angle Radial Sparse Parallel Magnetic Resonance Imaging. Investigative Radiology. 53(1). 52–61. 45 indexed citations

20.

Kim, Eun Jeong, Sung Hun Kim, Bong Joo Kang, et al.. (2015). Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma. Journal of Magnetic Resonance Imaging. 42(6). 1666–1678. 85 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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