Tullie Murrell
Impact in
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- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Advanced Neuroimaging Techniques and Applications
- MRI in cancer diagnosis
Papers in
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- Human Pose and Action Recognition 1
- Multimodal Machine Learning Applications 1
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- Nuclear Physics and Applications 1
- Co-authors
- Matthew J. Muckley (2 shared papers)Anuroop Sriram (2 shared papers)Daniel K. Sodickson (2 shared papers)Florian Knöll (2 shared papers)Michael P. Recht (2 shared papers)Nafissa Yakubova (1 shared paper)C. Lawrence Zitnick (1 shared paper)Michael Rabbat (1 shared paper)
- Journals
- Magnetic Resonance in Medicine (1 paper)Radiology Artificial Intelligence (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesIsraelGermany
In The Last Decade
Tullie Murrell
4 papers receiving 179 citations
Peers
Comparison fields: 5 of 50
- Radiology, Nuclear Medicine and Imaging 109
- Health Informatics 6
- Computer Vision and Pattern Recognition 31
- Biomedical Engineering 37
- Computational Mechanics 16
Countries citing papers authored by Tullie Murrell
This map shows the geographic impact of Tullie Murrell'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 Tullie Murrell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tullie Murrell more than expected).
Fields of papers citing papers by Tullie Murrell
This network shows the impact of papers produced by Tullie Murrell. 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 Tullie Murrell. The network helps show where Tullie Murrell may publish in the future.
Co-authors
The 25 scholars most cited alongside Tullie Murrell, 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 | 2020 | 132 | |
| 2 | 2021 | 28 | |
| 3 | 2022 | 21 | |
| 4 | MRI Banding Removal via Adversarial Training | 2020 | 1 |
About Tullie Murrell
Tullie Murrell is a scholar working on Computer Vision and Pattern Recognition, Radiation, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Biomedical Engineering, having authored 4 papers that have together received 182 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (1 paper), Medical Imaging Techniques and Applications (1 paper), Nuclear Physics and Applications (1 paper), Advanced X-ray and CT Imaging (1 paper), Human Pose and Action Recognition (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (109 citations), Health Informatics (6 citations), Computer Vision and Pattern Recognition (31 citations), Biomedical Engineering (37 citations) and Computational Mechanics (16 citations). Tullie Murrell has collaborated with scholars based in United States, Israel and Germany. Frequent co-authors include Matthew J. Muckley, Anuroop Sriram, Daniel K. Sodickson, Florian Knöll, Michael P. Recht, Nafissa Yakubova, C. Lawrence Zitnick, Michael Rabbat, Jure Žbontar and Aaron Defazio. Their work appears in journals such as Magnetic Resonance in Medicine, Radiology Artificial Intelligence and Neural Information Processing Systems.
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.