Modeling the MRI gradient system with a temporal convolutional network: Improved reconstruction by prediction of readout gradient errors
Temporal convolutional networks can more accurately model gradient system behavior than existing linear methods and may be used to retrospectively correct gradient errors.
Authors
- John C. Gore (ORCID: https://orcid.org/0000-0002-9480-0900)
- Hannah Alderson (ORCID: https://orcid.org/0009-0009-0408-4831)
- Mark D. Does (ORCID: https://orcid.org/0000-0003-2701-6208)
- Kevin D. Harkins (ORCID: https://orcid.org/0000-0003-3579-9273)
- Jonathan B. Martin (ORCID: https://orcid.org/0000-0002-9384-8056)
Institutions
- Vanderbilt University (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Magnetic Resonance in Medicine
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1002/mrm.70044
- Citations
- 2
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- National Institute of Biomedical Imaging and Bioengineering