Thomas Schultz

2.8k total citations
89 papers, 1.5k citations indexed

About

Thomas Schultz is a scholar working on Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Thomas Schultz has authored 89 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Computer Vision and Pattern Recognition and 11 papers in Cognitive Neuroscience. Recurrent topics in Thomas Schultz's work include Advanced Neuroimaging Techniques and Applications (42 papers), Advanced MRI Techniques and Applications (17 papers) and Retinal Imaging and Analysis (10 papers). Thomas Schultz is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (42 papers), Advanced MRI Techniques and Applications (17 papers) and Retinal Imaging and Analysis (10 papers). Thomas Schultz collaborates with scholars based in Germany, United States and Netherlands. Thomas Schultz's co-authors include Gordon Kindlmann, Hans‐Peter Seidel, Keith J. Dreyer, Tobias Schmidt‐Wilcke, Mannudeep K. Kalra, James H. Thrall, Holger Theisel, Elkan F. Halpern, Lara Schlaffke and Ingrid Hotz and has published in prestigious journals such as PLoS ONE, NeuroImage and Scientific Reports.

In The Last Decade

Thomas Schultz

85 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas Schultz Germany 25 914 226 177 177 161 89 1.5k
C.-F. Westin United States 23 2.4k 2.6× 999 4.4× 779 4.4× 113 0.6× 224 1.4× 41 3.9k
Stanley Durrleman France 26 531 0.6× 546 2.4× 334 1.9× 413 2.3× 79 0.5× 98 2.4k
Lauren J. O’Donnell United States 36 3.4k 3.7× 324 1.4× 1.1k 6.4× 118 0.7× 90 0.6× 142 4.6k
Carlos Alberola‐López Spain 26 818 0.9× 1.2k 5.5× 137 0.8× 313 1.8× 81 0.5× 169 2.6k
Olivier Commowick France 24 787 0.9× 693 3.1× 113 0.6× 166 0.9× 122 0.8× 73 1.7k
Brad Davis United States 12 549 0.6× 685 3.0× 119 0.7× 114 0.6× 99 0.6× 24 1.4k
Suyash P. Awate United States 22 545 0.6× 715 3.2× 161 0.9× 188 1.1× 26 0.2× 85 1.5k
Vincent Arsigny France 14 959 1.0× 974 4.3× 260 1.5× 205 1.2× 23 0.1× 19 2.3k
Krishna S. Nayak United States 40 3.6k 4.0× 262 1.2× 154 0.9× 458 2.6× 293 1.8× 219 5.8k
Santiago Aja‐Fernández Spain 27 1.5k 1.6× 1.3k 5.6× 170 1.0× 187 1.1× 46 0.3× 113 2.8k

Countries citing papers authored by Thomas Schultz

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Schultz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Schultz

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Schultz. A scholar is included among the top collaborators of Thomas Schultz 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 Schultz. Thomas Schultz 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.
Javanmardi, Behnam, Tzung‐Chien Hsieh, Silvan Mertes, et al.. (2025). GestaltGAN: synthetic photorealistic portraits of individuals with rare genetic disorders. European Journal of Human Genetics. 33(3). 377–382. 4 indexed citations
2.
Schultz, Thomas, et al.. (2024). No code machine learning: validating the approach on use-case for classifying clavicle fractures. Clinical Imaging. 112. 110207–110207. 2 indexed citations
3.
Schultz, Thomas, et al.. (2021). Parcellation-Free prediction of task fMRI activations from dMRI tractography. Medical Image Analysis. 76. 102317–102317. 2 indexed citations
4.
Martin, Pascal, G Hagberg, Thomas Schultz, et al.. (2020). T2-Pseudonormalization and Microstructural Characterization in Advanced Stages of Late-infantile Metachromatic Leukodystrophy. Clinical Neuroradiology. 31(4). 969–980. 12 indexed citations
5.
Schultz, Thomas & Anna Vilanova. (2018). Diffusion MRI visualization. NMR in Biomedicine. 32(4). e3902–e3902. 11 indexed citations
6.
Stirnberg, Rüdiger, Robbert Harms, Thomas Schultz, et al.. (2018). Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging. Frontiers in Neuroscience. 12. 650–650. 22 indexed citations
7.
Lim, Lek‐Heng, et al.. (2017). Versatile, robust, and efficient tractography with constrained higher-order tensor fODFs. International Journal of Computer Assisted Radiology and Surgery. 12(8). 1257–1270. 12 indexed citations
8.
Schultz, Thomas & Samuel Groeschel. (2013). Auto-calibrating Spherical Deconvolution Based on ODF Sparsity. Lecture notes in computer science. 16(Pt 1). 663–670. 9 indexed citations
9.
Panagiotaki, Eleftheria, et al.. (2012). CDMRI 2012. MICCAI 2012 Workshop on Computational Diffusion MRI.
10.
Estépar, Raúl San Jośe, et al.. (2012). Computational vascular morphometry for the assessment of pulmonary vascular disease based on scale-space particles. Europe PMC (PubMed Central). 1479–1482. 36 indexed citations
11.
Schultz, Thomas. (2012). Learning a Reliable Estimate of the Number of Fiber Directions in Diffusion MRI. Lecture notes in computer science. 15(Pt 3). 493–500. 17 indexed citations
12.
Schultz, Thomas. (2011). Segmenting Thalamic Nuclei: What Can We Gain from HARDI?. Lecture notes in computer science. 14(Pt 2). 141–148. 5 indexed citations
13.
Schultz, Thomas, Carl‐Fredrik Westin, & Gordon Kindlmann. (2010). Multi-Diffusion-Tensor Fitting via Spherical Deconvolution: A Unifying Framework. Lecture notes in computer science. 13(Pt 1). 674–681. 29 indexed citations
14.
Dang, Pragya A., et al.. (2009). Informatics in Radiology. Radiographics. 29(5). 1233–1246. 39 indexed citations
15.
Schultz, Thomas, et al.. (2008). A tear-free, SPF50 sunscreen product. Cutaneous and Ocular Toxicology. 27(3). 231–239. 4 indexed citations
16.
Dang, Pragya A., Mannudeep K. Kalra, Michael A. Blake, et al.. (2008). Natural Language Processing Using Online Analytic Processing for Assessing Recommendations in Radiology Reports. Journal of the American College of Radiology. 5(3). 197–204. 28 indexed citations
17.
Schultz, Thomas & Hans‐Peter Seidel. (2008). Estimating Crossing Fibers: A Tensor Decomposition Approach. IEEE Transactions on Visualization and Computer Graphics. 14(6). 1635–1642. 83 indexed citations
18.
Schultz, Thomas, Holger Theisel, & Hans‐Peter Seidel. (2007). Topological Visualization of Brain Diffusion MRI Data. IEEE Transactions on Visualization and Computer Graphics. 13(6). 1496–1503. 34 indexed citations
19.
Mehta, Amit, Thomas Schultz, Keith J. Dreyer, & Robert A. Novelline. (2000). Empowering the online educator. Academic Radiology. 7(3). 196–197. 3 indexed citations
20.

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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