Daniel Rueckert

92.9k total citations · 17 hit papers
591 papers, 44.6k citations indexed

About

Daniel Rueckert is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Daniel Rueckert has authored 591 papers receiving a total of 44.6k indexed citations (citations by other indexed papers that have themselves been cited), including 306 papers in Radiology, Nuclear Medicine and Imaging, 236 papers in Computer Vision and Pattern Recognition and 90 papers in Artificial Intelligence. Recurrent topics in Daniel Rueckert's work include Medical Image Segmentation Techniques (190 papers), Advanced MRI Techniques and Applications (128 papers) and Advanced Neuroimaging Techniques and Applications (77 papers). Daniel Rueckert is often cited by papers focused on Medical Image Segmentation Techniques (190 papers), Advanced MRI Techniques and Applications (128 papers) and Advanced Neuroimaging Techniques and Applications (77 papers). Daniel Rueckert collaborates with scholars based in United Kingdom, Germany and United States. Daniel Rueckert's co-authors include Joseph V. Hajnal, David J. Hawkes, Paul Aljabar, José Caballero, David Hill, Ben Glocker, Wenzhe Shi, Alexander Hammers, Mark Jenkinson and Martin O. Leach and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and The Lancet.

In The Last Decade

Daniel Rueckert

565 papers receiving 43.7k citations

Hit Papers

Tract-based spatial statistics: Voxelwise analysis of mul... 1999 2026 2008 2017 2006 2016 1999 2016 2009 1000 2.0k 3.0k 4.0k 5.0k
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.

  1. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
    2006 5.3k citations Daniel Rueckert et al. profile →
  2. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
    2016 4.3k citations Daniel Rueckert et al. profile →
  3. Nonrigid registration using free-form deformations: application to breast MR images
    1999 3.7k citations Daniel Rueckert, David Hill et al. IEEE Transactions on Medical Imaging profile →
  4. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
    2016 2.1k citations Christian Ledig, Daniel Rueckert et al. profile →
  5. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
    2009 1.7k citations Daniel Rueckert et al. profile →
  6. Medical Image Computing and Computer-Assisted Intervention
    2009 1.3k citations Daniel Rueckert et al. profile →
  7. Attention gated networks: Learning to leverage salient regions in medical images
    2019 1.2k citations Jo Schlemper, Daniel Rueckert et al. profile →
  8. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
    2017 803 citations Jo Schlemper, Joseph V. Hajnal et al. IEEE Transactions on Medical Imaging profile →
  9. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion
    2006 670 citations Joseph V. Hajnal, Daniel Rueckert et al. profile →
  10. Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy
    2009 663 citations Joseph V. Hajnal, Daniel Rueckert et al. profile →
  11. Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics
    2007 488 citations Daniel Rueckert et al. profile →
  12. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
    2017 438 citations Wenjia Bai, Antonio de Marvao et al. IEEE Transactions on Medical Imaging profile →
  13. Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease
    2018 432 citations Sofia Ira Ktena, Ricardo Guerrero et al. profile →
  14. Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
    2018 382 citations Jo Schlemper, Joseph V. Hajnal et al. IEEE Transactions on Medical Imaging profile →
  15. Self-supervised learning for medical image analysis using image context restoration
    2019 340 citations Daniel Rueckert et al. profile →
  16. End-to-end privacy preserving deep learning on multi-institutional medical imaging
    2021 255 citations Daniel Rueckert et al. profile →
  17. Evaluation and mitigation of the limitations of large language models in clinical decision-making
    2024 200 citations Daniel Rueckert et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Rueckert United Kingdom 88 20.2k 16.2k 6.6k 5.5k 5.3k 591 44.6k
Dinggang Shen United States 114 20.7k 1.0× 16.3k 1.0× 13.0k 2.0× 12.1k 2.2× 4.4k 0.8× 1.3k 53.1k
Ron Kikinis United States 86 14.6k 0.7× 6.6k 0.4× 7.3k 1.1× 1.9k 0.3× 3.3k 0.6× 377 35.7k
D. Louis Collins Canada 85 13.8k 0.7× 7.3k 0.5× 12.1k 1.8× 1.4k 0.3× 2.5k 0.5× 515 37.2k
Christos Davatzikos United States 95 12.7k 0.6× 7.8k 0.5× 10.9k 1.6× 3.1k 0.6× 1.7k 0.3× 644 34.2k
Sébastien Ourselin United Kingdom 76 10.2k 0.5× 5.1k 0.3× 3.7k 0.6× 2.8k 0.5× 1.7k 0.3× 812 26.7k
James C. Gee United States 64 14.3k 0.7× 5.3k 0.3× 9.3k 1.4× 1.8k 0.3× 3.1k 0.6× 370 33.1k
Max A. Viergever Netherlands 85 17.9k 0.9× 13.9k 0.9× 2.3k 0.3× 3.6k 0.7× 1.2k 0.2× 563 35.8k
Guido Gerig United States 69 9.0k 0.4× 5.8k 0.4× 5.3k 0.8× 1.2k 0.2× 3.5k 0.7× 282 23.4k
Ferenc A. Jólesz United States 119 21.0k 1.0× 4.4k 0.3× 7.3k 1.1× 891 0.2× 3.4k 0.6× 459 44.8k
U. Rajendra Acharya Singapore 125 14.6k 0.7× 7.8k 0.5× 23.4k 3.5× 11.6k 2.1× 566 0.1× 966 63.3k

Countries citing papers authored by Daniel Rueckert

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Rueckert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Rueckert

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Rueckert. A scholar is included among the top collaborators of Daniel Rueckert 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 Daniel Rueckert. Daniel Rueckert 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.
Scott, Andrew D., Zohya Khalique, Ranil de Silva, et al.. (2025). Accelerating cardiac diffusion tensor imaging with deep learning-based tensor de-noising and breath hold reduction. A step towards improved efficiency and clinical feasibility. Journal of Cardiovascular Magnetic Resonance. 27(2). 101971–101971.
2.
Nicolini, Luis Fernando, et al.. (2025). Neural network surrogate and projected gradient descent for fast and reliable finite element model calibration: A case study on an intervertebral disc. Computers in Biology and Medicine. 186. 109646–109646. 1 indexed citations
3.
Würm, Reinhard, Robert Graf, Jan Valošek, et al.. (2025). Automatic segmentation of spinal cord lesions in MS: A robust tool for axial T2-weighted MRI scans. Imaging Neuroscience. 3. 1 indexed citations
4.
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
5.
Hinterwimmer, Florian, et al.. (2024). The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review. JMIR mhealth and uhealth. 13. e60521–e60521. 2 indexed citations
6.
7.
Menten, Martin J., et al.. (2021). Deep Learning Prediction Of Age And Sex From Optical Coherence Tomography. 238–242. 7 indexed citations
8.
Qiu, Huaqi, Qin Chen, Andreas Schuh, Kerstin Hammernik, & Daniel Rueckert. (2021). Learning Diffeomorphic and Modality-invariant Registration using B-splines.. 645–664. 23 indexed citations
9.
Fetit, Ahmed E., et al.. (2020). Training deep segmentation networks on texture-encoded input: application to neuroimaging of the developing neonatal brain. Spiral (Imperial College London). 230–240.
10.
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
11.
Duan, Jinming, Ghalib Bello, Jo Schlemper, et al.. (2019). Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach. IEEE Transactions on Medical Imaging. 38(9). 2151–2164. 137 indexed citations
12.
Ktena, Sofia Ira, Markus D. Schirmer, Mark R. Etherton, et al.. (2019). Brain Connectivity Measures Improve Modeling of Functional Outcome After Acute Ischemic Stroke. Stroke. 50(10). 2761–2767. 16 indexed citations
13.
Garcia, Kara, Emma C. Robinson, Dimitrios Alexopoulos, et al.. (2018). Dynamic patterns of cortical expansion during folding of the preterm human brain. Proceedings of the National Academy of Sciences. 115(12). 3156–3161. 86 indexed citations
14.
Koikkalainen, Juha, Hanneke Rhodius‐Meester, Antti Tolonen, et al.. (2016). Differential diagnosis of neurodegenerative diseases using structural MRI data. NeuroImage Clinical. 11. 435–449. 123 indexed citations
15.
Karim, Rashed, Raad Mohiaddin, & Daniel Rueckert. (2007). Automatic Segmentation of the Left Atrium. 19. 302–3.
16.
Sermesant, Maxime, Kawal Rhode, Shreya Hegde, et al.. (2004). Electromechanical Modelling of the Myocardium using XMR Interventional Imaging. Journal of Cardiovascular Magnetic Resonance. 6(1). 1 indexed citations
17.
Brady, Michael, et al.. (2004). Simultaneous segmentation and registration for medical image. Lecture notes in computer science. 3216. 663–670. 8 indexed citations
18.
Hartkens, Thomas, et al.. (2003). Information Extraction from Medical Images (IXI): Developing an e-Science Application Based on the Globus Toolkit. UCL Discovery (University College London). 10 indexed citations
19.
Hajnal, Joseph V., et al.. (2002). A dynamic brain atlas. Lecture notes in computer science. 2488. 532–539. 9 indexed citations
20.
Rueckert, Daniel, et al.. (1997). Automatic tracking of the aorta in cardiovascular MR images using deformable models. IEEE Transactions on Medical Imaging. 16(5). 581–590. 74 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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