Deep learning-based acceleration of muscle water T2 mapping in patients with neuromuscular diseases by more than 50% - translating quantitative MRI from research to clinical routine
AI-based acceleration of CS data allows for scan time reduction of more than 50% for T2w mapping in the thigh muscles of NMD patients without compromising quantitative validity.
Authors
- Jan S. Kirschke (ORCID: https://orcid.org/0000-0002-7557-0003)
- Marcus Deschauer (ORCID: https://orcid.org/0000-0001-6116-6790)
- Dimitrios C. Karampinos (ORCID: https://orcid.org/0000-0003-4922-3662)
- Jonathan Stelter (ORCID: https://orcid.org/0000-0003-3335-3331)
- Federica Montagnese (ORCID: https://orcid.org/0000-0002-7291-5408)
- Joachim Schmitt
- Benedikt G. H. Schoser (ORCID: https://orcid.org/0000-0002-2757-8131)
- Sarah Schlaeger
- Dominik Weidlich (ORCID: https://orcid.org/0000-0001-7842-2682)
- Kilian Weiss (ORCID: https://orcid.org/0000-0003-4295-4585)
- Claus Zimmer (ORCID: https://orcid.org/0000-0002-4805-9103)
Institutions
- TUM Klinikum (DE)
- LMU Klinikum (DE)
- Friedrich Baur Stiftung (DE)
- Philips (Germany) (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1371/journal.pone.0318599
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00