Tullie Murrell
Impact in
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- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
- MRI in cancer diagnosis
- Radiomics and Machine Learning in Medical Imaging
- Advanced Neuroimaging Techniques and Applications
- Cardiac Imaging and Diagnostics
Papers in
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- Medical Imaging Techniques and Applications 3
- Advanced MRI Techniques and Applications 2
- Radiomics and Machine Learning in Medical Imaging 1
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- Advanced X-ray and CT Imaging 2
- Co-authors
- Matthew J. Muckley (2 shared papers)Anuroop Sriram (2 shared papers)Florian Knöll (2 shared papers)Daniel K. Sodickson (2 shared papers)Michael Rabbat (1 shared paper)Michael P. Recht (2 shared papers)Nafissa Yakubova (1 shared paper)C. Lawrence Zitnick (1 shared paper)
- Journals
- Radiology Artificial Intelligence (1 paper)Magnetic Resonance in Medicine (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesIsraelGermany
In The Last Decade
Tullie Murrell
4 papers receiving 175 citations
Peers
Comparison fields: 5 of 53
- Radiology, Nuclear Medicine and Imaging 131
- Health Informatics 7
- Computer Vision and Pattern Recognition 34
- Biomedical Engineering 42
- 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 | 128 | |
| 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 Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Computer Vision and Pattern Recognition, Radiation and Artificial Intelligence, having authored 4 papers that have together received 178 indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (3 papers), Advanced MRI Techniques and Applications (2 papers), Advanced X-ray and CT Imaging (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Multimodal Machine Learning Applications (1 paper), Human Pose and Action Recognition (1 paper), Anomaly Detection Techniques and Applications (1 paper) and Nuclear Physics and Applications (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (131 citations), Health Informatics (7 citations), Computer Vision and Pattern Recognition (34 citations), Biomedical Engineering (42 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, Florian Knöll, Daniel K. Sodickson, Michael Rabbat, Michael P. Recht, Nafissa Yakubova, C. Lawrence Zitnick, Aaron Defazio and Jure Žbontar. Their work appears in journals such as Radiology Artificial Intelligence, Magnetic Resonance in Medicine 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.