Deep-learning-accelerated T1-MPRAGE MRI for quantification and visual grading of cerebral volume in memory loss patients
DL-MPRAGE offers quantitatively and qualitatively equivalent volumetric estimation compared to STD-MPRAGE while improving spatial resolution and acquisition speed for patients undergoing evaluation for memory loss.
Authors
- Azadeh Hajati (ORCID: https://orcid.org/0000-0002-1196-6957)
- Sook Wah Yee (ORCID: https://orcid.org/0000-0001-8121-8746)
- Azadeh Tabari (ORCID: https://orcid.org/0000-0002-5685-6401)
- Nelson Gil (ORCID: https://orcid.org/0000-0002-0771-2292)
- Dominik 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)
- Min Lang
- Sittaya Buathong (ORCID: https://orcid.org/0009-0007-5467-7825)
Institutions
- Harvard University (US)
- Massachusetts General Hospital (US)
- Medical Solutions (GB)
- Siemens (United States) (US)
- Siemens Healthcare (United States) (US)
Publication Details
- Journal
- Radiology Advances
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1093/radadv/umaf022
- Citations
- 3
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Siemens Healthineers
- National Institutes of Health