Heidi Daniel
Impact in
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- MRI in cancer diagnosis
- Radiomics and Machine Learning in Medical Imaging
- Advanced MRI Techniques and Applications
- Advanced Neuroimaging Techniques and Applications
- Medical Imaging Techniques and Applications
Papers in
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- MRI in cancer diagnosis 10
- Advanced Neuroimaging Techniques and Applications 2
- Advanced MRI Techniques and Applications 2
- Medical Imaging Techniques and Applications 1
- Radiomics and Machine Learning in Medical Imaging 1
- Co-authors
- Wolfgang Lederer (10 shared papers)Sebastian Bickelhaupt (10 shared papers)Frederik Bernd Laun (10 shared papers)Stefan Delorme (8 shared papers)Heinz-Peter Schlemmer (4 shared papers)Anne Stieber (5 shared papers)Klaus Hermann Maier-Hein (4 shared papers)Daniel Paech (4 shared papers)
In The Last Decade
Heidi Daniel
10 papers receiving 402 citations
Peers
Comparison fields: 5 of 35
- Radiology, Nuclear Medicine and Imaging 339
- Health Informatics 4
- Artificial Intelligence 59
- Pulmonary and Respiratory Medicine 23
- Oncology 19
Countries citing papers authored by Heidi Daniel
This map shows the geographic impact of Heidi Daniel'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 Heidi Daniel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heidi Daniel more than expected).
Fields of papers citing papers by Heidi Daniel
This network shows the impact of papers produced by Heidi Daniel. 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 Heidi Daniel. The network helps show where Heidi Daniel may publish in the future.
Co-authors
The 25 scholars most cited alongside Heidi Daniel, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 117 | |
| 2 | 2015 | 108 | |
| 3 | 2018 | 84 | |
| 4 | 2016 | 36 | |
| 5 | 2017 | 32 | |
| 6 | 2017 | 18 | |
| 7 | 2020 | 6 | |
| 8 | 2016 | 5 | |
| 9 | 2019 | 3 | |
| 10 | 2019 | 1 |
About Heidi Daniel
Heidi Daniel is a scholar working on Radiology, Nuclear Medicine and Imaging, Insect Science, Renewable Energy, Sustainability and the Environment, Obstetrics and Gynecology and Anesthesiology and Pain Medicine, having authored 10 papers that have together received 410 indexed citations. Recurring topics across this work include MRI in cancer diagnosis (10 papers), Advanced Neuroimaging Techniques and Applications (2 papers), Advanced MRI Techniques and Applications (2 papers), Medical Imaging Techniques and Applications (1 paper) and Radiomics and Machine Learning in Medical Imaging (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (339 citations), Health Informatics (4 citations), Artificial Intelligence (59 citations), Pulmonary and Respiratory Medicine (23 citations) and Oncology (19 citations). Heidi Daniel has collaborated with scholars based in Germany, France and China. Frequent co-authors include Wolfgang Lederer, Sebastian Bickelhaupt, Frederik Bernd Laun, Stefan Delorme, Heinz-Peter Schlemmer, Anne Stieber, Klaus Hermann Maier-Hein, Daniel Paech, Heinz‐Peter Schlemmer and Tristan Anselm Kuder. Their work appears in journals such as Radiology, Journal of Magnetic Resonance Imaging, RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, Scientific Reports and Journal of Computer Assisted Tomography.
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.