Physics-informed deep learning reconstruction for ultrafast clinical 3D fluid-attenuated inversion recovery brain MRI
Deep learning reconstruction of 3D-FLAIR provides higher image quality compared to a state-of-the-art technique with 30% less scan time while maintaining excellent agreement and interchangeability in quantitative evaluation.
Authors
- Azadeh Hajati (ORCID: https://orcid.org/0000-0002-1196-6957)
- Florence L. Chiang (ORCID: https://orcid.org/0000-0002-3405-5590)
- Dominik M. Nickel (ORCID: https://orcid.org/0000-0002-0360-7233)
- John Conklin (ORCID: https://orcid.org/0000-0001-9921-2086)
- Shohei Fujita (ORCID: https://orcid.org/0000-0003-4276-6226)
- Wei‐Ching Lo (ORCID: https://orcid.org/0000-0002-1893-0500)
- Bryan Clifford (ORCID: https://orcid.org/0000-0002-1966-0581)
- Stephen Cauley (ORCID: https://orcid.org/0000-0002-6147-6016)
- Susie Y. Huang (ORCID: https://orcid.org/0000-0003-2950-7254)
- Sittaya Buathong (ORCID: https://orcid.org/0009-0007-5467-7825)
Institutions
- Harvard University (US)
- Massachusetts General Hospital (US)
- Harvard–MIT Division of Health Sciences and Technology (US)
- Medical Solutions (GB)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- Siemens (United States) (US)
- Siemens Healthcare (United States) (US)
- Massachusetts Institute of Technology (US)
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- Radiology Advances
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1093/radadv/umaf016
- Citations
- 3
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Siemens Healthineers