Florian Schaff

799 total citations
40 papers, 545 citations indexed

About

Florian Schaff is a scholar working on Radiation, Biomedical Engineering and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Florian Schaff has authored 40 papers receiving a total of 545 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Radiation, 23 papers in Biomedical Engineering and 19 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Florian Schaff's work include Advanced X-ray Imaging Techniques (27 papers), Advanced X-ray and CT Imaging (23 papers) and Medical Imaging Techniques and Applications (15 papers). Florian Schaff is often cited by papers focused on Advanced X-ray Imaging Techniques (27 papers), Advanced X-ray and CT Imaging (23 papers) and Medical Imaging Techniques and Applications (15 papers). Florian Schaff collaborates with scholars based in Germany, Australia and Sweden. Florian Schaff's co-authors include Franz Pfeiffer, Martin Bech, Christoph Jud, Manuel Guizar‐Sicairos, Tobias Lasser, Marianne Liebi, Paul Zaslansky, Friedrich Prade, Yash Sharma and Sascha Senck and has published in prestigious journals such as Nature, Physical Review Letters and Applied Physics Letters.

In The Last Decade

Florian Schaff

33 papers receiving 531 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Florian Schaff Germany 13 296 210 133 69 54 40 545
Christoph Jud Germany 12 330 1.1× 248 1.2× 189 1.4× 22 0.3× 48 0.9× 31 644
Alexander Sasov Belgium 13 428 1.4× 415 2.0× 374 2.8× 129 1.9× 23 0.4× 54 913
А. В. Бузмаков Russia 13 214 0.7× 201 1.0× 185 1.4× 9 0.1× 96 1.8× 101 577
Lorenzo Massimi United Kingdom 16 377 1.3× 244 1.2× 199 1.5× 8 0.1× 147 2.7× 54 766
Hans Deyhle Switzerland 18 473 1.6× 526 2.5× 275 2.1× 14 0.2× 61 1.1× 87 1.2k
Julian Moosmann Germany 15 216 0.7× 256 1.2× 131 1.0× 11 0.2× 113 2.1× 89 750
Jeroen Cant Belgium 4 237 0.8× 371 1.8× 399 3.0× 9 0.1× 58 1.1× 10 750
Paul Evans United Kingdom 14 212 0.7× 254 1.2× 189 1.4× 58 0.8× 70 1.3× 68 521
W. Graeff Poland 17 432 1.5× 286 1.4× 232 1.7× 118 1.7× 229 4.2× 102 1.0k
M. C. Nichols United States 13 181 0.6× 196 0.9× 135 1.0× 15 0.2× 127 2.4× 32 523

Countries citing papers authored by Florian Schaff

Since Specialization
Citations

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

Fields of papers citing papers by Florian Schaff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Florian Schaff

This figure shows the co-authorship network connecting the top 25 collaborators of Florian Schaff. A scholar is included among the top collaborators of Florian Schaff 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 Florian Schaff. Florian Schaff 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.
Gassert, Florian T., Thorsten Sellerer, Rafael Schick, et al.. (2025). Estimating Total Lung Volume from Pixel-Level Thickness Maps of Chest Radiographs Using Deep Learning. Radiology Artificial Intelligence. 7(4). e240484–e240484. 1 indexed citations
2.
3.
Koehler, Thomas, et al.. (2024). Streak artefact removal in x‐ray dark‐field computed tomography using a convolutional neural network. Medical Physics. 51(10). 7404–7414.
4.
Lasser, Tobias, et al.. (2024). Improving Automated Hemorrhage Detection at Sparse-View CT via U-Net–based Artifact Reduction. Radiology Artificial Intelligence. 6(4). e230275–e230275. 4 indexed citations
5.
Schaff, Florian, Martin Dierolf, Benedikt Günther, et al.. (2024). Feasibility of Dark-Field Radiography to Enhance Detection of Nondisplaced Fractures. Radiology. 311(2). e231921–e231921. 1 indexed citations
6.
Hofmann, Felix, Theresa Urban, Franz Pfeiffer, et al.. (2024). Optimizing convolutional neural networks for Chronic Obstructive Pulmonary Disease detection in clinical computed tomography imaging. Computers in Biology and Medicine. 185. 109533–109533. 4 indexed citations
7.
Sauter, Andreas, et al.. (2024). Improving image quality of sparse-view lung tumor CT images with U-Net. European Radiology Experimental. 8(1). 54–54. 1 indexed citations
8.
Hofmann, Florian, Theresa Urban, Friedrich Pfeiffer, et al.. (2023). Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging. RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. 195(S 01). S35–S35. 3 indexed citations
9.
Schaff, Florian, et al.. (2022). X-ray computed tomography with seven degree of freedom robotic sample holder. Engineering Research Express. 4(3). 35022–35022. 10 indexed citations
10.
Schaff, Florian, Kaye S. Morgan, David M. Paganin, & Marcus J. Kitchen. (2020). Spectral x-ray imaging: Conditions under which propagation-based phase-contrast is beneficial. Physics in Medicine and Biology. 65(20). 205006–205006. 5 indexed citations
11.
Li, Heyang, et al.. (2020). Quantitative material decomposition using linear iterative near-field phase retrieval dual-energy x-ray imaging. Physics in Medicine and Biology. 65(18). 185014–185014. 6 indexed citations
12.
Mathavan, Neashan, Mikael J. Turunen, Manuel Guizar‐Sicairos, et al.. (2018). The compositional and nano-structural basis of fracture healing in healthy and osteoporotic bone. Scientific Reports. 8(1). 12 indexed citations
13.
Schaff, Florian, et al.. (2018). Brain Connectivity Exposed by Anisotropic X-ray Dark-field Tomography. Scientific Reports. 8(1). 14345–14345. 14 indexed citations
14.
Birnbacher, Lorenz, Florian Schaff, Lukas B. Gromann, et al.. (2018). Simultaneous wood and metal particle detection on dark-field radiography. European Radiology Experimental. 2(1). 1–1. 13 indexed citations
15.
Schaff, Florian, Friedrich Prade, Yash Sharma, Martin Bech, & Franz Pfeiffer. (2017). Non-iterative Directional Dark-field Tomography. Scientific Reports. 7(1). 3307–3307. 17 indexed citations
16.
Sharma, Yash, et al.. (2017). Design of Acquisition Schemes and Setup Geometry for Anisotropic X-ray Dark-Field Tomography (AXDT). Scientific Reports. 7(1). 3195–3195. 17 indexed citations
17.
Schaff, Florian, Martin Bech, Paul Zaslansky, et al.. (2015). Six-dimensional real and reciprocal space small-angle X-ray scattering tomography. Nature. 527(7578). 353–356. 133 indexed citations
18.
Wolf, Johannes, Jonathan I. Sperl, Florian Schaff, et al.. (2015). Lens-term- and edge-effect in X-ray grating interferometry. Biomedical Optics Express. 6(12). 4812–4812. 11 indexed citations
19.
Baum, Thomas, Elena Eggl, Andreas Malecki, et al.. (2015). X-ray Dark-Field Vector Radiography—A Novel Technique for Osteoporosis Imaging. Journal of Computer Assisted Tomography. 39(2). 286–289. 12 indexed citations
20.
Malecki, Andreas, Elena Eggl, Florian Schaff, et al.. (2014). Correlation of X-Ray Dark-Field Radiography to Mechanical Sample Properties. Microscopy and Microanalysis. 20(5). 1528–1533. 9 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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