Rapid and robust quantitative cartilage assessment for the clinical setting: deep learning-enhanced accelerated T2 mapping
DL CartiGram significantly accelerates T2 mapping, while still assuring excellent repeatability and reproducibility. Combined with the semi-automatic post-processing pipeline, it emerges as a promising tool for quantitative T2 cartilage biomarker assessment in clinical settings.
Authors
- Valentina Pedoia (ORCID: https://orcid.org/0000-0002-9745-955X)
- Anthony A. Gatti (ORCID: https://orcid.org/0000-0001-6717-8979)
- Florian Wiesinger (ORCID: https://orcid.org/0000-0002-5597-6057)
- Sharmila Majumdar (ORCID: https://orcid.org/0000-0002-0201-871X)
- Norberto Malpica (ORCID: https://orcid.org/0000-0003-4618-7459)
- Eugenia Sánchez-Lacalle
- Mario Padrón
- Arjun Desai (ORCID: https://orcid.org/0000-0003-0645-3257)
- Akshay Sanjay Chaudhari (ORCID: https://orcid.org/0000-0002-3667-6796)
- Bruno Nunes (ORCID: https://orcid.org/0009-0006-5479-4346)
- Maggie Fung
- Laura Carretero-Gómez (ORCID: https://orcid.org/0009-0007-6998-1045)
Institutions
- University of California, San Francisco (US)
- Universidad Rey Juan Carlos (ES)
- General Electric (Spain) (ES)
- Instituto Geológico y Minero de España (ES)
- Clínica Rementería (ES)
- Siemens Healthineers (Germany) (DE)
- Stanford University (US)
Publication Details
- Journal
- Skeletal Radiology
- Published
- 2025-09-18
- DOI
- https://doi.org/10.1007/s00256-025-05034-w
- Citations
- 1
- Primary Topic
- Osteoarthritis Treatment and Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 1.67