Thomas Küstner

2.6k total citations · 1 hit paper
68 papers, 1.4k citations indexed

About

Thomas Küstner is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Thomas Küstner has authored 68 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 61 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Biomedical Engineering and 13 papers in Computer Vision and Pattern Recognition. Recurrent topics in Thomas Küstner's work include Medical Imaging Techniques and Applications (37 papers), Advanced MRI Techniques and Applications (37 papers) and Radiomics and Machine Learning in Medical Imaging (21 papers). Thomas Küstner is often cited by papers focused on Medical Imaging Techniques and Applications (37 papers), Advanced MRI Techniques and Applications (37 papers) and Radiomics and Machine Learning in Medical Imaging (21 papers). Thomas Küstner collaborates with scholars based in Germany, United Kingdom and United States. Thomas Küstner's co-authors include Sergios Gatidis, Bin Yang, Konstantin Nikolaou, Fritz Schick, Karim Armanious, Kerstin Hammernik, Daniel Rueckert, Claudia Prieto, René M. Botnar and Petros Martirosian and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Thomas Küstner

64 papers receiving 1.4k 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.

TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI

2025 26 citations Tugba Akinci D’Antonoli, Matthias Jung et al. Radiology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Thomas Küstner Germany	23	1.1k	287	223	123	122	68	1.4k
Kyunghyun Sung United States	23	1.4k 1.2×	348 1.2×	286 1.3×	158 1.3×	208 1.7×	91	2.0k
Erich Kobler Germany	9	1.0k 0.9×	302 1.1×	193 0.9×	145 1.2×	52 0.4×	19	1.3k
Hanns‐Christian Breit Switzerland	11	541 0.5×	278 1.0×	82 0.4×	42 0.3×	93 0.8×	41	797
Michael Perkuhn Germany	16	630 0.6×	185 0.6×	97 0.4×	54 0.4×	57 0.5×	18	958
Jonathan I. Sperl Germany	16	834 0.7×	401 1.4×	44 0.2×	69 0.6×	62 0.5×	46	1.2k
Lauren Kim United States	14	469 0.4×	133 0.5×	207 0.9×	60 0.5×	347 2.8×	35	1.0k
Marco Nolden Germany	13	571 0.5×	317 1.1×	252 1.1×	22 0.2×	78 0.6×	30	1.1k
Leonard Sunwoo South Korea	21	695 0.6×	175 0.6×	96 0.4×	42 0.3×	92 0.8×	72	1.2k
Hyun‐Seok Min South Korea	15	149 0.1×	223 0.8×	206 0.9×	328 2.7×	73 0.6×	45	809

Countries citing papers authored by Thomas Küstner

Since Specialization

Citations

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

Fields of papers citing papers by Thomas Küstner

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Küstner

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

D’Antonoli, Tugba Akinci, Matthias Jung, Alexander Rau, et al.. (2025). TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI. Radiology. 314(2). e241613–e241613. 26 indexed citations breakdown →

Wetzl, Jens, Rolf Gebker, Christoph Tillmanns, et al.. (2025). Towards high resolution cardiac T1 mapping utilizing deep learning-based image reconstruction. Journal of Cardiovascular Magnetic Resonance. 27. 101523–101523.

Hagen, Florian, Saif Afat, Jean‐François Paul, et al.. (2024). Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography. Diagnostic and Interventional Imaging. 106(2). 68–75. 12 indexed citations

Schroeder, Christopher, Sergios Gatidis, Olga Kelemen, et al.. (2024). Tumour-informed liquid biopsies to monitor advanced melanoma patients under immune checkpoint inhibition. Nature Communications. 15(1). 8750–8750. 12 indexed citations

Küstner, Thomas, Kerstin Hammernik, Daniel Rueckert, Tobias Hepp, & Sergios Gatidis. (2024). Predictive uncertainty in deep learning–based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set. Magnetic Resonance in Medicine. 92(1). 289–302. 3 indexed citations

Ozenne, Valéry, Soumaya Sridi, Thomas Küstner, et al.. (2024). Fully automated contrast selection of joint bright- and black-blood late gadolinium enhancement imaging for robust myocardial scar assessment. Magnetic Resonance Imaging. 109. 256–263. 4 indexed citations

Herrmann, Judith, Haidara Almansour, Mike Notohamiprodjo, et al.. (2024). Reducing energy consumption in musculoskeletal MRI using shorter scan protocols, optimized magnet cooling patterns, and deep learning sequences. European Radiology. 35(4). 1993–2004. 3 indexed citations

Hammernik, Kerstin, et al.. (2024). Attention incorporated network for sharing low-rank, image and k-space information during MR image reconstruction to achieve single breath-hold cardiac Cine imaging. Computerized Medical Imaging and Graphics. 120. 102475–102475. 2 indexed citations

Cobos, Erick, et al.. (2023). Avoiding Shortcut-Learning by Mutual Information Minimization in Deep Learning-Based Image Processing. IEEE Access. 11. 64070–64086. 4 indexed citations

10.

Hepp, Tobias, et al.. (2023). Deep learning-based age estimation from clinical Computed Tomography image data of the thorax and abdomen in the adult population. PLoS ONE. 18(11). e0292993–e0292993. 4 indexed citations

11.

Othman, Ahmed E., Dominik Rath, Brigitte Gückel, et al.. (2023). Free-breathing Arterial Spin Labeling MRI for the Detection of Pulmonary Embolism. Radiology. 307(3). e221998–e221998. 7 indexed citations

12.

Muñoz, Camila, Haikun Qi, Gastão Cruz, et al.. (2021). Self-supervised learning-based diffeomorphic non-rigid motion estimation for fast motion-compensated coronary MR angiography. Magnetic Resonance Imaging. 85. 10–18. 13 indexed citations

13.

Reinert, Christian Philipp, Konstantin Nikolaou, Christina Pfannenberg, et al.. (2021). Detection of lung lesions in breath-hold VIBE and free-breathing Spiral VIBE MRI compared to CT. Insights into Imaging. 12(1). 175–175. 7 indexed citations

14.

Küstner, Thomas, Christopher Gilliam, Haikun Qi, et al.. (2020). Deep-learning based motion-corrected image reconstruction in 4D magnetic resonance imaging of the body trunk. Research Portal (King's College London). 976–985. 4 indexed citations

15.

Armanious, Karim, Tobias Hepp, Thomas Küstner, et al.. (2020). Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks. EJNMMI Research. 10(1). 53–53. 47 indexed citations

16.

Fuin, Niccolò, Aurélien Bustin, Thomas Küstner, et al.. (2020). A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography. Magnetic Resonance Imaging. 70. 155–167. 35 indexed citations

17.

Küstner, Thomas, Tobias Hepp, Marc Fischer, et al.. (2020). Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies. Radiology Artificial Intelligence. 2(6). e200010–e200010. 30 indexed citations

18.

Küstner, Thomas, et al.. (2019). Retrospective correction of motion‐affected MR images using deep learning frameworks. Magnetic Resonance in Medicine. 82(4). 1527–1540. 80 indexed citations

19.

Küstner, Thomas, Tobias Hepp, Petros Martirosian, et al.. (2018). ImFEATbox: a toolbox for extraction and analysis of medical image features. International Journal of Computer Assisted Radiology and Surgery. 13(12). 1881–1893. 9 indexed citations

20.

Fayad, Hadi, Holger Schmidt, Thomas Küstner, & Dimitris Visvikis. (2016). 4-Dimensional MRI and Attenuation Map Generation in PET/MRI with 4-Dimensional PET-Derived Deformation Matrices: Study of Feasibility for Lung Cancer Applications. Journal of Nuclear Medicine. 58(5). 833–839. 10 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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