Deep learning reconstruction for accelerated 3-D magnetic resonance cholangiopancreatography
Deep learning-accelerated 3-D MRCP reduced acquisition time by 62%, minimized artifacts, and preserved bile and pancreatic duct visibility, supporting its adoption in routine clinical practice.
Authors
- Konstantin Nikolaou (ORCID: https://orcid.org/0000-0003-2668-7325)
- Sebastian Gassenmaier (ORCID: https://orcid.org/0000-0003-3701-2227)
- Reza Dehdab (ORCID: https://orcid.org/0000-0002-3496-8808)
- Judith Herrmann (ORCID: https://orcid.org/0000-0003-1535-8980)
- Haidara Almansour (ORCID: https://orcid.org/0000-0003-1672-3855)
- Jan M. Brendel (ORCID: https://orcid.org/0000-0002-2729-610X)
- Elisabeth Weiland (ORCID: https://orcid.org/0000-0001-7517-1291)
- Sebastian Werner (ORCID: https://orcid.org/0000-0003-4542-6462)
- Stephan Ursprung
- Dominik Nickel (ORCID: https://orcid.org/0000-0002-0360-7233)
- Saif Afat
Institutions
- Siemens Healthineers (Germany) (DE)
- University of Tübingen (DE)
Publication Details
- Journal
- La radiologia medica
- Published
- 2025-03-18
- DOI
- https://doi.org/10.1007/s11547-025-01987-z
- Citations
- 6
- Primary Topic
- Gallbladder and Bile Duct Disorders
- Type
- article
- Field-Weighted Citation Impact
- 9.11
Funders
- Universitätsklinikum Tübingen