Automated Field of View Prescription for Whole-body Magnetic Resonance Imaging Using Deep Learning Based Body Region Segmentations
DL-based three-dimensional anatomic segmentations enable accurate and reliable multistation FoV prescription for WB-MRI, achieving expert-level performance while significantly reducing manual workload. Automated FoV planning has the potential to standardize WB-MRI acquisition, reduce interoperator variability, and enhance workflow efficiency, thereby facilitating broader clinical adoption.
Authors
- Luca Salhöfer (ORCID: https://orcid.org/0000-0002-5376-3154)
- René Hosch (ORCID: https://orcid.org/0000-0003-1760-2342)
- Johannes Haubold (ORCID: https://orcid.org/0000-0003-4843-5911)
- Lale Umutlu (ORCID: https://orcid.org/0000-0001-5215-7171)
- Anton Sheahan Quinsten (ORCID: https://orcid.org/0000-0001-8168-8406)
- Kai Naßenstein (ORCID: https://orcid.org/0000-0002-4005-3196)
- Jannis Straus
- Michael Forsting (ORCID: https://orcid.org/0000-0002-5584-9824)
- Mathias Meetschen (ORCID: https://orcid.org/0000-0002-0719-2207)
- Katarzyna Borys (ORCID: https://orcid.org/0000-0001-6987-6041)
- Felix Nensa (ORCID: https://orcid.org/0000-0002-5811-7100)
- Judith Kohnke (ORCID: https://orcid.org/0009-0009-8826-3481)
- Yutong Wen (ORCID: https://orcid.org/0009-0003-8557-6665)
- Christian Bojahr (ORCID: https://orcid.org/0009-0006-9635-4795)
Institutions
- Essen University Hospital (DE)
Publication Details
- Journal
- Investigative Radiology
- Published
- 2025-09-16
- DOI
- https://doi.org/10.1097/rli.0000000000001236
- Citations
- 3
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.08