Rosette Trajectory MRI Reconstruction with Vision Transformers
This approach outperforms established deep learning techniques for normalized root mean squared error, peak signal-to-noise ratio, and entropy-based image quality scores; offers better runtime performance; and remains competitive with respect to other metrics.
Authors
- Onur Özyurt (ORCID: https://orcid.org/0000-0001-9479-6190)
- Uzay E. Emir (ORCID: https://orcid.org/0000-0001-5376-0431)
- Ulaş Bağcı (ORCID: https://orcid.org/0000-0001-7379-6829)
- Cengizhan Öztürk (ORCID: https://orcid.org/0000-0002-6966-0774)
- Muhammed Fikret Yalcinbas
Institutions
- Northwestern University (US)
- University of North Carolina at Chapel Hill (US)
- Intel (United States) (US)
- University of Cambridge (GB)
- Boğaziçi University (TR)
Publication Details
- Journal
- Tomography
- Published
- 2025-04-01
- DOI
- https://doi.org/10.3390/tomography11040041
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00