An end‐to‐end deep learning method for reconstructing SMS‐PI accelerated musculoskeletal MRI
We extend state-of-the-art DL reconstruction frameworks by integrating slice separation directly into the model for SMS acquisitions. Our E2E DL approach achieves clinical-grade image quality at 8-fold acceleration across 20 subjects, reducing acquisition time by 27%. Preliminary results suggest potential for further acceleration up to 12-fold, demonstrating significant advancement beyond existing DL techniques.
Authors
- Jan Fritz (ORCID: https://orcid.org/0000-0003-4456-3043)
- Jan Vosshenrich (ORCID: https://orcid.org/0000-0003-3323-8472)
- Mary T. Bruno (ORCID: https://orcid.org/0000-0003-1625-0293)
- Gregor Koerzdoerfer (ORCID: https://orcid.org/0000-0002-8881-9517)
- Mahmoud Mostapha (ORCID: https://orcid.org/0000-0001-6019-1492)
- Yannik Leonhardt (ORCID: https://orcid.org/0000-0003-0028-6654)
- Nirmal Janardhanan
- Esther Raithel (ORCID: https://orcid.org/0000-0001-7878-4974)
- Mariappan S. Nadar
Institutions
- Siemens (Germany) (DE)
- University Hospital of Basel (CH)
- TUM Klinikum (DE)
- NYU Langone Health (US)
- Siemens (United States) (US)
- Siemens Healthcare (United States) (US)
- Siemens Healthineers (Germany) (DE)
- Technical University of Munich (DE)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/mp.70178
- Citations
- 2
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.72